Intelligent control method and system for hydroelectric generating set speed regulator

Abstract

The invention relates to the technical field of power generation equipment control, and discloses an intelligent control method and system for a hydroelectric generating set speed regulator, and the method comprises the steps: employing an introduction variable structure and variable parameter control technology, and dynamically adjusting the control parameters of a power closed loop and an opening closed loop; real-time calculation and feedback control are carried out, and a speed regulator responds to a power grid frequency modulation Operating data of key parts of the speed regulator are collected in real time through a sensor, feature extraction and mode recognition are conducted on the data in combination with an artificial intelligence algorithm, and the operating state and potential faults of the speed regulator are recognized; constructing a fault tree model based on the speed regulator state monitoring parameters, carrying out hierarchical analysis on the speed regulator, and positioning the specific position and reason of the fault; and verifying and optimizing the fault diagnosis result, and generating a targeted fault processing suggestion. The system comprises a dynamic adjustment module, a state evaluation module and a fault analysis module. According to the invention, the operation efficiency and safety of the hydroelectric generating set speed regulator in a complex power grid environment are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power generation equipment control, and in particular to an intelligent control method and system for a speed governor of a hydropower unit. Background Art

[0002] With the gradual expansion of the scale of power construction and the in-depth promotion of the "unmanned and less-staffed" production management mode of large hydropower, the requirements for equipment operation and management level are getting higher and higher. Ensuring the safe and high-quality operation of the system, improving the reliability of equipment operation, reducing the cost of equipment operation and maintenance, and improving the economic benefits of the enterprise are the focus of power plants and one of the measures to improve their core competitiveness. The way of regulating power of domestic hydropower units is to monitor large closed-loop regulation, without deeply considering the characteristics of the control object, and the means adopted are relatively single; due to pulse power regulation, it can be seen from the water pressure recording that each time the guide vane switch is started, a large reverse regulation will be generated; also due to hydraulic factors, the opening closed loop or simple power closed loop is difficult to achieve power stability and precise regulation within a wide range of loads; often resulting in large overshoot, too fast or too slow, power fluctuations cannot be stabilized, etc., which have a certain impact on the power grid. The speed of the hydraulic follow-up system of the speed governor is poor. There are certain studies at home and abroad on the online monitoring and intelligent diagnosis of turbine speed governors, but overall there is no systematization and specialization, let alone productization and practical wide application.

[0003] The existing active power control mode of hydropower units is active power PID of the monitoring system + speed regulator opening mode control; after years of operation, although the active power control of the unit is stable and reliable, after the hydropower unit participated in the frequency regulation auxiliary market of the Southern Power Grid, the AGC frequency regulation performance indicators did not meet the standards many times. There are the following problems that need to be further optimized: (1) The active power PID control response rate of the monitoring system is obviously slow, the active power regulation dead zone is large, the active power control accuracy is not high, and the AGC frequency regulation performance indicators are poor, which cannot fully meet the requirements of the power grid. (2) The hydraulic factors and their impact on the turbine are complex, especially when the "water hammer effect" is obvious at high head, the active power regulation of the hydropower unit has reverse regulation phenomenon, and even active power oscillation occurs. (3) When the total regulation AGC sends values ​​frequently and the regulation direction is opposite, the unit has no time to respond, and the frequency regulation effect is very poor, and an optimization strategy is needed.

[0004] At the same time, for large hydropower plants, ensuring the healthy operation of the speed control system is the basis for ensuring the safe and stable operation of the unit. However, at present, the speed control system of hydropower plants is limited to the monitoring and alarm of some components, and the function is single. For example, there are only comprehensive alarms for electrical cabinets and mechanical cabinets, and detailed fault information still requires users to check in the cabinet by themselves, and no pre-diagnosis function and "condition maintenance" strategy can be provided. How to use the acquired equipment monitoring data to correctly evaluate the operating status of the speed control system, predict the system status changes, and analyze the abnormal problems existing in the operation of the speed control system has become an urgent problem to be solved. To achieve automatic fault diagnosis and early warning in a relatively complete sense, it is necessary to conduct in-depth research from monitoring technology, signal processing, diagnostic models, and fault mechanisms. At the same time, with the inevitable trend of the comprehensive development of traditional hydropower plants towards intelligent hydropower plants, the transformation of traditional hydropower plants using new generation information technologies such as cloud computing, big data, and artificial intelligence will effectively promote the pace of the "unmanned and less-staffed" production management mode of power plants.

[0005] Prior art 1, Chinese patent, application number: 202410482663.5 discloses a method for suppressing low-frequency oscillation of active power of a hydropower unit and a speed governor control device, including the steps of: introducing a tailwater gate water level deviation change signal that has been filtered and calculated into the controller output signal of the hydropower unit speed governor, and monitoring the change of the downstream gate chamber water level in real time, and feeding it back to the speed governor of the hydropower unit, so as to suppress the active oscillation of the hydropower unit. Although it can respond to the unstable flow state in the tailwater flow channel in a timely and accurate manner, realize the active stability of the hydropower unit through feedback, and improve the performance and reliability of the hydropower unit, it has important application value in suppressing the active oscillation of the hydropower unit in the hydropower generation system, has the potential to improve the operational stability of the hydropower generation system, and brings significant economic benefits to the hydropower generation industry; however, due to its relatively simple structure, the speed governor has slow response and insufficient accuracy.

[0006] Prior art 2, Chinese patent, application number: 202410319986.2 discloses a method, system, storage medium and electronic device for parameter setting of an additional controller of a hydropower unit speed governor, including the following steps: constructing respective linearization models for multi-machine systems, and constructing an ultra-low frequency oscillation mode analysis model of a multi-machine system with a high proportion of hydropower for the multi-machine system; based on the phase compensation principle, constructing an additional controller model of the speed governor for increasing the damping torque coefficient of the system, the additional controller model of the speed governor includes a bandpass filter and a phase compensation link; establishing a unified frequency model of the multi-machine system, and constructing a second-order characteristic equation of the electromechanical oscillation ring, and establishing a calculation model for compensating the damping torque coefficient of the additional controller of the speed governor based on the obtained ultra-low frequency oscillation mode of the system; based on the calculation model for compensating the damping torque coefficient of the additional controller of the speed governor, establishing a parameter setting model of the additional controller of the speed governor, and performing parameter setting on all additional controllers of the speed governor. Although it has the technical effect of accurately and clearly setting the parameters of the additional controller of the speed regulator and effectively suppressing ultra-low frequency oscillations without affecting the primary frequency regulation performance of the system; it lacks technical features related to fault prediction and intelligent decision-making, resulting in the overall performance and reliability of the speed regulator need to be further improved.

[0007] Prior art three, Chinese patent, application number: 202410975477.5 discloses a method for optimizing the control parameters of a speed regulator for primary frequency regulation of a hydropower unit, comprising the following steps: constructing a primary frequency regulation simulation model of a hydropower unit; keeping the given frequency f of the parallel PID speed regulator unchanged, setting the measurement frequency f to change in a step at the beginning of the simulation to form a frequency disturbance Δf, running the primary frequency regulation simulation model of the hydropower unit, and measuring the output power sequence Psim at the turbine simulation model end; calculating the residual square value of the output power sequence Psim and the target power sequence Ptar, and taking the mean of the residual square value to obtain the mean square error; using the mean square error as the objective function of the primary frequency regulation optimization of the hydropower unit, and optimizing the primary frequency regulation simulation model of the hydropower unit; substituting the optimized control parameters into the parallel PID speed regulator simulation model, and reapplying the same frequency disturbance Δf to it, and measuring the output power Psim2 at the turbine simulation model end; comparing the output power Psim and Psim2 responses before and after optimization. Although the water diversion system simulation model and turbine simulation model adopted can significantly improve the simulation speed and reduce the consumption of computer performance; however, the model construction is complicated, which increases the time cost of parameter optimization to a certain extent.

[0008] At present, the existing technologies 1, 2 and 3 have the problem that the intelligent control degree of the speed governor is low, resulting in low overall performance and reliability of the speed governor. Therefore, the present invention provides a method and system for intelligent control of a speed governor of a hydropower unit. Summary of the invention

[0009] The main purpose of the present invention is to provide a method and system for intelligent control of a speed governor of a hydropower unit, so as to solve the problem that the speed governor control in the prior art has a low intelligence level, resulting in low overall performance and reliability of the speed governor.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] A method for intelligently controlling a speed governor of a hydropower unit, the method comprising:

[0012] The variable structure and variable parameter control technology is introduced to dynamically adjust the control parameters of the power closed loop and the opening closed loop; real-time calculation and feedback control, the speed regulator responds to the grid frequency modulation command;

[0013] The sensors collect the operating data of the key components of the speed regulator in real time, including temperature, pressure, vibration and flow parameters. The collected parameters are stored and preprocessed using big data, and artificial intelligence algorithms are used to extract features and recognize patterns in the data to identify the operating status and potential faults of the speed regulator. The health status of the speed regulator is scored in real time by building a status assessment model to generate an operating status report.

[0014] Based on the parameters of speed governor status monitoring, a fault tree model is constructed to perform hierarchical analysis on the speed governor to locate the specific location and cause of the fault. Combined with expert knowledge in the field of speed governors, an expert system is constructed to verify and optimize the fault diagnosis results and generate targeted fault handling suggestions. Based on the fault diagnosis results, the control parameters or operating mode of the speed governor are automatically adjusted to achieve adaptive regulation.

[0015] As a further improvement of the present invention, the process of dynamically adjusting the control parameters of the power closed loop and the opening closed loop includes:

[0016] The speed governor receives the power set value from the monitoring system, and the received power set value will be processed by standard and converted into a standardized signal inside the speed governor; the speed governor performs ramp processing on the power set value, and the power set value after ramp processing will directly act on the guide vane opening to form a pre-adjustment of the guide vane opening;

[0017] In the power closed-loop mode, the speed governor's regulation goal is to make the actual output power of the generator consistent with the given power; the speed governor monitors the actual output power of the generator in real time, compares it with the given power, and calculates the power deviation; based on the power deviation, the speed governor adjusts the guide vane opening through the PID control algorithm to make the actual power gradually approach the given power;

[0018] On the basis of power closed loop, the speed governor needs to perform closed-loop control on the guide vane opening to ensure that the guide vane opening matches the power demand; the speed governor monitors the actual guide vane opening in real time, compares it with the target opening, and calculates the opening deviation; based on the opening deviation, the speed governor fine-tunes the guide vane opening to ensure coordination between the opening and the power demand;

[0019] During the power closed-loop and opening closed-loop control process, the speed governor dynamically adjusts the parameters of the PID controller according to the dynamic response; when the actual output power is consistent with the given power, the speed governor stops adjusting and maintains the current guide vane opening and power output; the speed governor feeds back the final power and opening status to the monitoring system.

[0020] As a further improvement of the present invention, the process of the ramp processing of the given power includes:

[0021] Based on historical data and current system status, neural networks are used to predict the changing trend of power demand, and the prediction results are used to dynamically adjust the slope of the ramp. In the process of ramp generation, adaptive smoothing filters are used to adjust the filter parameters in real time according to the dynamic response characteristics of the system. The power given signal is divided into multiple ramp segments, and different slopes are set for each segment according to the current load characteristics and dynamic response capabilities of the system.

[0022] Use the real-time collected system status data to accurately predict future power demand; combine the feedforward prediction signal with PID feedback control to form a dynamic collaborative optimization mechanism. The feedforward signal is used to adjust the system in advance; PID feedback control is used to correct the error in the feedforward prediction; according to the real-time operating status of the system, the weight of the feedforward signal and the feedback signal is dynamically adjusted;

[0023] Through the online performance evaluation mechanism, the system's response time, overshoot, and key stability indicators are monitored in real time; according to the evaluation results, the parameters of the ramp generator and the feedforward prediction model are dynamically adjusted; the system's response speed, stability, and energy consumption targets are incorporated into the multi-objective optimization framework; and the optimal balance point between multiple objectives is found through a genetic algorithm.

[0024] As a further improvement of the present invention, the filtering process of power feedback includes:

[0025] Adopting an adaptive filtering strategy based on a time window, the filter time constant is dynamically adjusted to maintain real-time performance under different working conditions. The filter time constant is adaptively adjusted according to the frequency characteristics of power fluctuations.

[0026] The power signal is decomposed into different frequency scales through wavelet transform to filter out high-frequency clutter and burrs; the Kalman filter is used to dynamically predict the trend of power fluctuation based on the historical data and current data of power feedback, and smooth it. By combining wavelet transform and Kalman filtering, high-frequency noise is filtered out while tracking the main line of power fluctuation;

[0027] By monitoring the filtered power feedback signal in real time, the degree of matching with the actual power fluctuation is evaluated, and the filtering parameters are dynamically adjusted according to the evaluation results.

[0028] As a further improvement of the present invention, the power regulation strategy of the speed regulator includes:

[0029] The speed regulator receives the power mode activation / deactivation signal, power setting mode selection signal, power regulation enable signal, and 4-20mA power setting analog signal sent by the monitoring system; the speed regulator analyzes and determines the current working mode and power regulation target;

[0030] In the power closed-loop mode, the speed governor automatically performs the primary and secondary frequency regulation functions, canceling the original frequency regulation blocking strategy of the monitoring system; the speed governor dynamically adjusts the output power according to the received power given signal to ensure the optimal response and regulation performance of the unit;

[0031] The speed regulator feeds back the current power mode status and 4-20mA power setting analog signal to the monitoring system for comparison and confirmation of the power setting, thus realizing the handshake mechanism between the speed regulator and the monitoring system.

[0032] According to the adjustment results of the speed regulator, the monitoring system optimizes the AGC setting dead zone, adjustment dead zone, allocation strategy and frequency modulation locking logic to improve the response speed and adjustment accuracy of the AGC; at the same time, the monitoring system sends a load given signal to the speed regulator according to the AGC load distribution principle.

[0033] As a further improvement of the present invention, the process of coordinating primary frequency modulation with power regulation includes:

[0034] The speed regulator adopts the power mode, and the adjustment object of the primary frequency regulation is the active power of the unit; when the primary frequency regulation is in a steady load state and there is no external load setting, the adjustment amount of the primary frequency regulation will not be pulled back by the power regulation; when there is an external load regulation instruction, the speed regulator will give priority to responding to the power regulation request regardless of whether the primary frequency regulation is in action;

[0035] The load increase and decrease caused by the primary frequency regulation is superimposed according to the principle shown in the drawing; steady-state load regulation, when the load is in a steady state, the adjustment amount of the primary frequency regulation should be independent of the power regulation; dynamic load regulation, when there is an external load regulation instruction, the speed regulator should respond to the power regulation first;

[0036] The speed regulator calculates the power deviation in real time and quickly adjusts the guide vane opening according to the deviation value to compensate for the delay caused by the mechanical dead zone and the electrical dead zone. It dynamically adjusts the action amount of the primary frequency regulation according to the change of the grid frequency, monitors the action amount of the primary frequency regulation in real time, and optimizes the frequency regulation strategy according to historical data.

[0037] Monitor the action amount of primary frequency regulation in real time and compare it with the theoretical calculated amount; automatically adjust the frequency regulation strategy according to the power grid assessment standards.

[0038] As a further improvement of the present invention, the process of switching between the speed regulator power mode and the opening mode includes:

[0039] The speed regulator is regulated in power mode under normal operation, and the speed regulator directly responds to the power command; the opening closed loop is used as a backup plan for the power mode, and maintains the basic load adjustment capability when the power mode is abnormal;

[0040] When the power closed-loop regulation system detects a fault or anomaly, it automatically identifies and records the fault type and time; after the fault is identified, the speed regulator automatically switches to the opening mode; in some cases, the operator chooses to manually switch to the opening mode according to the actual situation;

[0041] When the fault that causes the automatic exit of the power closed loop is repaired, the fault recovery status is automatically detected; no matter whether the fault recovery is completed automatically or manually intervened, it will not automatically switch from the opening mode to the power mode; after the operator's confirmation, the speed regulator switches from the opening mode to the power mode; after the mode switching is completed, the operating status of the speed regulator is monitored in real time.

[0042] As a further improvement of the present invention, the variable parameter control process of the speed regulator includes the following steps:

[0043] Get the current speed regulator parameter value and determine whether the current parameter value is within the preset reasonable range; if it is not within the range, it needs to be adjusted;

[0044] If the parameter value is within the range, it is further determined whether it meets the set judgment standard; if not, the parameter needs to be adjusted; according to the judgment result, the speed regulator parameter is adjusted to change in the direction of meeting the standard or target value;

[0045] Repeat the above steps until the speed regulator parameters are stable within a reasonable range and meet the judgment criteria.

[0046] As a further improvement of the present invention, the process of identifying the operating state and potential faults of the speed regulator includes the following steps:

[0047] The sensors installed in various parts of the speed control system will monitor various physical signals in real time and convert the physical signals into electrical signals, which will be transmitted to the industrial computer through the speed control cabinet. After receiving the electrical signals, the industrial computer will perform preliminary data processing and analyze and extract characteristic parameters reflecting the operating status of the speed control system.

[0048] The extracted characteristic parameters are monitored in real time and compared with the preset normal operating range. If some parameters exceed the normal range, an alarm is triggered to indicate that there may be a fault. When an abnormality is detected, fault recording is automatically performed, that is, all relevant data before and after the fault occurs are recorded. The data is stored in the industrial computer and uploaded to the background comprehensive diagnosis system regularly.

[0049] After receiving these data, the background comprehensive diagnosis system analyzes them and generates status inspection logs and status development trend reports. Based on the analysis results, it provides auxiliary decision-making suggestions, such as whether status maintenance is needed and when to perform the maintenance. It is displayed through the human-machine interface and uploaded to the background comprehensive diagnosis system for reference by operation and maintenance personnel. All generated status data, analysis results and diagnostic reports are shared through the network, allowing local or remote user terminals to query and download data through the browser.

[0050] To achieve the above object, the present invention also provides the following technical solutions:

[0051] An intelligent control system for a speed governor of a hydropower unit, which is applied to the intelligent control method for a speed governor of a hydropower unit, and the intelligent control system for a speed governor of a hydropower unit comprises:

[0052] Dynamic adjustment module, used to dynamically adjust the control parameters of power closed loop and opening closed loop by introducing variable structure and variable parameter control technology; real-time calculation and feedback control, the speed regulator responds to the grid frequency regulation command;

[0053] The status assessment module is used to collect the operating data of the key components of the speed regulator in real time through sensors, store and preprocess the collected parameters using big data, and perform feature extraction and pattern recognition on the data in combination with artificial intelligence algorithms to identify the operating status and potential faults of the speed regulator; by building a status assessment model, the health status of the speed regulator is scored in real time and an operating status report is generated;

[0054] The fault analysis module is used to build a fault tree model based on the parameters of the speed regulator status monitoring, conduct hierarchical analysis of the speed regulator, and locate the specific location and cause of the fault; combine the expert knowledge in the field of speed regulators to build an expert system to verify and optimize the fault diagnosis results and generate targeted fault handling suggestions; according to the fault diagnosis results, the control parameters or operating mode of the speed regulator are automatically adjusted to achieve adaptive regulation.

[0055] The present invention provides an efficient control strategy, solves the problems of slow response and insufficient precision of the speed regulator, and significantly improves the performance of the speed regulator in power grid frequency regulation and power regulation; provides real-time data support for control optimization, ensures that the control strategy can be dynamically adjusted, and improves the operating reliability of the speed regulator through early fault warning; based on monitoring data, it realizes fault prediction and intelligent decision-making, further improves the overall performance and reliability of the speed regulator, and ensures that it can quickly respond to sudden faults in complex power grid environments. Through the organic combination of the three steps, the speed regulator intelligent control method realizes the intelligent management of the entire process from control optimization to state monitoring, and then to fault diagnosis and decision-making, significantly improving the operating efficiency and safety of the hydropower unit speed regulator in a complex power grid environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic diagram of the steps of an embodiment of the intelligent control method for the speed governor of a hydropower unit of the present invention;

[0057] Figure 2 A schematic flow chart of the steps of dynamically adjusting the control parameters of the power closed loop and the opening closed loop in one embodiment of the intelligent control method for the speed governor of a hydropower unit of the present invention;

[0058] Figure 3 A schematic diagram of dynamically adjusting control parameters of a power closed loop and an opening closed loop in one embodiment of an intelligent control method for a hydropower generator set speed regulator of the present invention;

[0059] Figure 4 This is a schematic diagram of the speed governor power given ramp processing and feedforward control principle of an embodiment of the hydropower unit speed governor intelligent control method of the present invention;

[0060] Figure 5 The principle of the signal transmission relationship between the speed regulator and the monitoring system in the power mode of an embodiment of the intelligent control method of the hydropower unit speed regulator of the present invention Figure 1 ;

[0061] Figure 6 The principle of the signal transmission relationship between the speed regulator and the monitoring system in the power mode of an embodiment of the intelligent control method of the hydropower unit speed regulator of the present invention Figure 2 ;

[0062] Figure 7 It is a schematic flow chart of the steps of adjusting the coordination relationship of primary frequency modulation power in one embodiment of the intelligent control method for the speed governor of a hydropower unit of the present invention;

[0063] Figure 8 A schematic flow chart of the coordination relationship between primary frequency regulation and power regulation in one embodiment of the intelligent control method for the speed governor of a hydropower unit of the present invention;

[0064] Fig. 9A schematic flow chart of the switching relationship between the power mode and the opening mode of an embodiment of the intelligent control method for the speed governor of a hydropower unit of the present invention;

[0065] Fig.10 A schematic flow chart of steps for identifying the operating status and potential faults of a speed governor of a hydropower unit according to an embodiment of the intelligent control method of the speed governor of the present invention;

[0066] Fig.11 A schematic flow chart of the steps of performing hierarchical analysis on a speed governor in accordance with an embodiment of the intelligent control method for a speed governor of a hydropower unit according to the present invention;

[0067] Fig.12 A schematic diagram of hierarchical analysis of a speed governor for a hydropower unit according to an embodiment of the intelligent control method for the speed governor of the present invention;

[0068] Fig.13 This is a functional module diagram of an embodiment of an intelligent control system for a hydropower unit speed governor of the present invention;

[0069] Fig.14 This is a functional module diagram of a data diagnosis module of an embodiment of an intelligent control system for a hydropower generator set speed governor according to the present invention;

[0070] Fig.15 This is a functional module diagram of a fault analysis module of an embodiment of an intelligent control system for a hydropower unit speed governor of the present invention;

[0071] Fig.16 This is a schematic diagram of a fault tree structure of a speed regulating system of an embodiment of an intelligent control system for a speed regulator of a hydropower unit according to the present invention;

[0072] Fig.17 This is a schematic diagram of the fault judgment principle of the guide vane and frequency measurement of an embodiment of the intelligent control system of the hydropower unit speed governor of the present invention;

[0073] Fig.18 This is a schematic diagram of the fault judgment principle of the main pressure regulating valve of an embodiment of the intelligent control system of the hydropower unit speed governor of the present invention;

[0074] Fig.19 This is a schematic diagram of the fault judgment of the PCC controller and the integrated control function block of an embodiment of the intelligent control system of the hydropower unit speed governor of the present invention;

[0075] Fig. 20 This is a schematic diagram of fault judgment of an emergency pressure distribution valve and a sectional closing valve in one embodiment of an intelligent control system for a hydropower unit speed governor of the present invention;

[0076] Fig.21 This is a schematic diagram of the oil level and oil pressure fault judgment principle of the oil pressure device of an embodiment of the intelligent control system of the hydropower unit speed governor of the present invention;

[0077] Fig. 22 This is a schematic diagram of the oil temperature and frequent pump start failure judgment of the oil pressure device in one embodiment of the intelligent control system of the hydropower unit speed governor of the present invention;

[0078] Fig.23 This is a schematic diagram of the fault judgment principle of startup failure and grid connection failure in one embodiment of the intelligent control system of the hydropower unit speed governor of the present invention;

[0079] Fig.24 This is a schematic diagram of abnormal load regulation judgment in power generation conditions in one embodiment of the intelligent control system for the speed governor of a hydropower unit of the present invention;

[0080] Fig.25 This is a schematic diagram of electrical faults in the speed regulating system of an embodiment of the intelligent control system for the speed regulator of a hydropower unit of the present invention;

[0081] Fig.26 This is a schematic diagram of monitoring and fault diagnosis of a proportional servo valve, a core component of a speed control system, in one embodiment of an intelligent control system for a hydropower unit speed controller of the present invention;

[0082] Fig. 27 This is a schematic diagram of monitoring and fault diagnosis of a main pressure regulating valve in one embodiment of an intelligent control system for a hydropower generator set speed governor according to the present invention;

[0083] Fig.28 This is a schematic diagram of electrical faults in the speed regulating system of an embodiment of the intelligent control system for the speed regulator of a hydropower unit of the present invention;

[0084] Fig.29 It is a structural schematic diagram of an embodiment of an electronic device of the present invention;

[0085] Fig.30 It is a schematic structural diagram of an embodiment of the storage medium of the present invention. DETAILED DESCRIPTION

[0086] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0087] The terms "first", "second" and "third" in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0088] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0089] like Figure 1 As shown, this embodiment provides an embodiment of a method for intelligently controlling a speed governor of a hydropower unit. In this embodiment, the method for intelligently controlling a speed governor of a hydropower unit specifically includes the following steps:

[0090] Step S1: Adopting variable structure and variable parameter control technology to dynamically adjust the control parameters of power closed loop and opening closed loop; real-time calculation and feedback control, the speed regulator responds to the grid frequency modulation command;

[0091] Step S2: collect the operating data of key components of the speed governor (such as servo valve, main pressure regulating valve, oil pressure device, etc.) in real time through sensors, including parameters such as temperature, pressure, vibration and flow; use big data to store and preprocess the collected parameters, combine artificial intelligence algorithms to extract features and perform pattern recognition on the data, and identify the operating status and potential faults of the speed governor; by building a status assessment model, the health status of the speed governor is scored in real time and an operating status report is generated;

[0092] The health status score calculation formula in the governor status assessment model is described as follows:

[0093]

[0094] Where H(t) represents the health status score of the speed regulator at time t, and its value range is [0,1], where 1 represents complete health and 0 represents complete failure; n represents the number of parameters collected by the sensor, such as temperature, pressure, vibration, etc.; w i (t) represents the adaptive weight of the i-th parameter at time t, indicating the importance of this parameter to the health status assessment, and the weight is dynamically adjusted over time; x i (t) represents the real-time value of the i-th parameter at time t, such as temperature, pressure, etc.; μ i represents the mean of the normal operating range of the ith parameter; k represents the slope parameter of the Sigmoid function, which controls the sensitivity of the function; represents the time derivative of the i-th parameter, indicating the rate of change of the parameter; α i represents the change rate weight of the i-th parameter, which is used to adjust the impact of the change rate on the health status score; σ i represents the standard deviation of the i-th parameter, indicating the volatility of the parameter; ∑ref represents the reference standard deviation, which is used to normalize the parameter volatility; β i represents the volatility weight of the i-th parameter, which is used to adjust the impact of volatility on the health status score;

[0095] The Sigmoid function is used to convert the parameter value x i (t) is mapped to the interval [0,1], indicating the degree to which the parameter deviates from the normal range. The time derivative is expressed by Capture the changing trend of parameters and combine the weight α i Adjust its impact on the health status score; volatility normalization means that Normalize parameter volatility and combine it with weight β i Adjust the impact of volatility on the score; adaptive weights represent w i (t) Dynamically adjust over time to reflect the relative importance of different parameters to health status assessment under different operating conditions; this formula is suitable for real-time assessment of the health status of the governor, and can comprehensively consider parameter values, change trends, volatility, and dynamic weights to generate an accurate health status score; by combining expert systems and fault tree models, fault diagnosis and adaptive regulation can be further optimized;

[0096] Step S3: Based on the parameters of the speed regulator status monitoring, a fault tree model is constructed to perform a hierarchical analysis on the speed regulator to locate the specific location and cause of the fault; based on expert knowledge in the field of speed regulators, an expert system is constructed to verify and optimize the fault diagnosis results and generate targeted fault handling suggestions; based on the fault diagnosis results, the control parameters or operating mode of the speed regulator are automatically adjusted to achieve adaptive regulation.

[0097] Preferably, the power control optimization of step S1 of this embodiment can dynamically adjust the control parameters of the power closed loop and the opening closed loop according to the real-time working conditions such as the power grid frequency modulation command and load fluctuation by introducing variable structure and variable parameter control technology, so as to ensure the fast response and precise regulation of the speed regulator under different working conditions; through real-time calculation and feedback control, the speed regulator can quickly respond to the power grid frequency modulation command, reduce the lag time of power regulation, and improve the regulation accuracy. Significance achieved: By dynamically adjusting the control parameters, the speed regulator can achieve fast response and precise regulation in a complex power grid environment, significantly improving its performance in power grid frequency modulation and power regulation; in view of the characteristics of asynchronous networking of the Yunnan power grid, the optimized control strategy can cope with the power fluctuation problem when the power grid is operating in an isolated island, and improve the adaptability of the speed regulator in a complex power grid environment; power control optimization is the core of the entire intelligent control method, and its optimization effect directly affects the performance of the speed regulator in power grid frequency modulation and power regulation, providing a solid foundation for state monitoring and fault diagnosis. Step S2: Intelligent state monitoring of the speed regulator collects the operating data of the key components of the speed regulator in real time through high-precision sensors, including parameters such as temperature, pressure, vibration, and flow, to ensure the comprehensiveness and accuracy of the data; uses big data technology to store and preprocess the collected data, combines artificial intelligence algorithms (such as machine learning and deep learning) to extract features and recognize patterns on the data, and identifies the operating status and potential faults of the speed regulator; by building a state evaluation model, the health status of the speed regulator is scored in real time, and an operating status report is generated to provide a basis for subsequent fault diagnosis and control parameter optimization. Significance achieved: Intelligent state monitoring provides real-time and dynamic data support for power control optimization, ensuring that the control strategy can be dynamically adjusted according to the actual operating status of the speed regulator; through big data analysis and artificial intelligence algorithms, it can identify the potential faults of the speed regulator in advance, provide early warning for fault diagnosis, help take preventive measures before the fault occurs, and avoid power regulation failure caused by faults; through real-time state evaluation, it can timely detect the abnormal operation of the speed regulator, ensure that the operating status of the speed regulator is always within the controllable range, and significantly improve its operating reliability. Step S3 is fault diagnosis and intelligent decision-making. Based on the speed governor status monitoring data, a fault tree model is constructed to perform a hierarchical analysis on the speed governor system to locate the specific location and cause of the fault. For example, by analyzing the oil pressure fluctuation data, it is determined whether the oil pressure device is faulty. In combination with expert knowledge in the field of speed governors, an expert system is constructed to verify and optimize the fault diagnosis results and generate targeted fault handling suggestions. For example, a cleaning or replacement plan is proposed for the servo valve stuck problem. According to the fault diagnosis results, the control parameters or operating mode of the speed governor are automatically adjusted to achieve adaptive adjustment of the system. For example, when a main pressure regulating valve fault is detected, the system switches to the backup control mode to ensure that the speed governor continues to operate stably.Significance achieved: Through fault tree analysis and expert system, the fault location and cause of the speed regulator can be quickly located, which can significantly shorten the troubleshooting time and improve the efficiency of fault handling; through intelligent decision-making and adaptive adjustment, the speed regulator can automatically adjust the operating mode in the event of a fault, ensuring that it continues to operate stably in a complex power grid environment, significantly improving the reliability and stability of the system; fault diagnosis and intelligent decision-making are the "brain" of the entire intelligent control method. Based on the status monitoring data, the operating status of the speed regulator is comprehensively analyzed, and an optimization plan is generated to realize the intelligent management of the speed regulator system.

[0098] In summary, this embodiment provides an efficient control strategy, solves the problems of slow response and insufficient precision of the speed regulator, and significantly improves the performance of the speed regulator in power grid frequency regulation and power regulation; provides real-time data support for control optimization, ensures that the control strategy can be dynamically adjusted, and improves the operating reliability of the speed regulator through early fault warning; based on monitoring data, it realizes fault prediction and intelligent decision-making, further improves the overall performance and reliability of the speed regulator, and ensures that it can quickly respond to sudden faults in complex power grid environments. Through the organic combination of the three steps, the speed regulator intelligent control method realizes the intelligent management of the entire process from control optimization to state monitoring, and then to fault diagnosis and decision-making, significantly improving the operating efficiency and safety of the hydropower unit speed regulator in a complex power grid environment.

[0099] This embodiment provides scientific guidance for the inspection and maintenance work, and it is necessary to realize the functions of speed regulating equipment operation trend analysis, fault warning and prediction, fault location, alarm information push, expert processing opinions, parameter optimization, state analysis and evaluation, and fault diagnosis. At the same time, after establishing an online monitoring and self-diagnosis system, the performance and health status of the speed governor system can be scientifically evaluated, and the causes of equipment failure, performance degradation and deterioration can be diagnosed and analyzed, so as to make the best decision for the inspection and fault handling of the speed governor system. Whether from the perspective of production technology management or the production efficiency of hydropower plants, the research on the online monitoring and self-diagnosis system of turbine speed governors is of great significance.

[0100] Furthermore, if Figure 2 As shown, the process of dynamically adjusting the control parameters of the power closed loop and the opening closed loop in step S1 specifically includes the following steps:

[0101] Step S11: The speed governor receives a power set value from the monitoring system, and the received power set value is subjected to standard processing and converted into a standardized signal inside the speed governor; the speed governor performs ramp processing on the power set value, and the ramp-processed power set value is directly applied to the PGV (guide vane opening) to form a pre-adjustment of the guide vane opening;

[0102] Step S12: In the power closed-loop mode, the speed governor's regulation target is to make the actual output power of the generator consistent with the given power; the speed governor monitors the actual output power of the generator in real time, compares it with the given power, and calculates the power deviation; according to the power deviation, the speed governor adjusts the guide vane opening through the PID (proportional-integral-differential) control algorithm to make the actual power gradually approach the given power;

[0103] Step S13: On the basis of the power closed loop, the speed governor needs to perform closed-loop control on the guide vane opening to ensure that the guide vane opening matches the power demand; the speed governor monitors the actual guide vane opening in real time, compares it with the target opening, and calculates the opening deviation; according to the opening deviation, the speed governor fine-tunes the guide vane opening to ensure coordination between the opening and the power demand;

[0104] Step S14: During the control process of the power closed loop and the opening closed loop, the speed governor dynamically adjusts the parameters of the PID controller according to the dynamic response; when the actual output power is consistent with the given power, the speed governor stops adjusting and maintains the current guide vane opening and power output; the speed governor feeds back the final power and opening status to the monitoring system.

[0105] Preferably, step S11 of this embodiment receives power setting and ramp processing, converts the power setting value into a standardized signal inside the speed regulator, ensures the uniformity and compatibility of the signal, and avoids control errors caused by different signal formats; through ramp processing, avoids the sudden change of the power setting value from causing impact on the system, and ensures the smooth transition of the system; the power setting value after ramp processing directly acts on PGV (guide vane opening), forms pre-regulation, speeds up the power regulation speed, and reduces the response time. Significance: Through ramp processing, reduce the impact of power mutation on the system, avoid system oscillation or instability; the pre-regulation mechanism enables the speed regulator to respond quickly to power demand and improve the dynamic performance of the system; ensure that the signal transmission between the speed regulator and the monitoring system is accurate and provides a reliable basis for control. Step S12 power closed-loop control, real-time monitoring of the deviation between the actual output power and the given power, ensure that the speed regulator can accurately identify the power demand; through the PID control algorithm, dynamically adjust the guide vane opening according to the power deviation, so that the actual power gradually approaches the given power; the PID control algorithm can quickly respond to the power deviation, achieve accurate power regulation, and ensure that the generator output power is consistent with the demand. Significance: Through power closed-loop control, the speed governor can accurately adjust the output power of the generator to meet the power grid's requirements for power stability; the PID control algorithm can quickly adjust the guide vane opening according to the power deviation, reduce power fluctuations, and improve the system's dynamic response capability; accurate power control helps maintain the frequency and voltage stability of the power grid and reduce the impact on the power grid. Step S13: Opening closed-loop control, real-time monitoring of the deviation between the actual opening of the guide vane and the target opening, to ensure that the speed governor can accurately adjust the guide vane opening; according to the opening deviation, the speed governor fine-tunes the guide vane opening to ensure the coordination between the opening and the power demand; through opening closed-loop control, the speed governor can further optimize the guide vane opening on the basis of the power closed loop to ensure the stability of power regulation. Significance: Through opening closed-loop control, the speed governor can accurately adjust the guide vane opening to avoid adverse effects on the system caused by excessive or too small opening; opening closed-loop control ensures the coordination between the guide vane opening and the power demand, and improves the overall control performance of the system; accurate opening control helps reduce system oscillation and ensures that the generator can operate stably under different working conditions. Step S14 dynamically adjusts PID parameters and feedback. According to the dynamic response of the system, the speed regulator dynamically adjusts the parameters of the PID controller (such as proportional coefficient, integral time and differential time) to optimize the control performance; the speed regulator can automatically adjust the control parameters according to the load changes, frequency fluctuations and other conditions of the system to ensure that the system can maintain good control performance under different working conditions; the speed regulator feeds back the final power and opening state to the monitoring system to ensure closed-loop management of the entire control process.Significance: Dynamically adjusting PID parameters enables the speed regulator to adapt to different operating conditions and improve the system's adaptability and robustness. By dynamically adjusting PID parameters, the speed regulator can achieve optimal control performance under different load conditions and reduce power fluctuations and opening deviations. The feedback mechanism ensures that the information transmission between the speed regulator and the monitoring system is accurate, achieving closed-loop management of the entire process and improving the reliability and stability of the system (the principle is shown in the attached figure). Figure 3 ).

[0106] In summary, in this embodiment, through standardized signal processing, ramp processing, PID control algorithm, opening closed-loop control and dynamic parameter adjustment, the speed regulator can achieve fast, accurate and stable power regulation; dynamically adjust PID parameters and adaptive control strategies so that the speed regulator can adapt to different operating conditions and improve the dynamic performance and robustness of the system. Significance: Improve the control accuracy of power and opening, ensure that the output power of the generator is consistent with the demand, and meet the power grid's requirements for power stability; optimize the system's dynamic response capability and stability, reduce power fluctuations and opening deviations, and improve the system's overall control performance; realize closed-loop management of the entire process, ensure that the information transmission between the speed regulator and the monitoring system is accurate, and improve the system's reliability and stability. Through the above steps, the speed regulator can achieve efficient, stable and accurate power control under power generation conditions, providing a strong guarantee for the stable operation of the power grid.

[0107] In this embodiment, when the power feedback and the target value differ very little (the fixed value can be set), the power closed-loop PID module is put into use for fine-tuning and precise adjustment. This combination of fast and slow can achieve rapid power regulation and is not easy to cause power overshoot. The PID parameter adaptability of the power closed loop is wider, and it is free from the influence of factors such as head change and Tw. Secondly, compared with the traditional power closed loop, the power regulation and frequency regulation parameters of this power closed loop are separated, and the power regulation module is similar to the downward shift of the power regulation function of the monitoring system, which is moved down to the speed regulator. The output of the power regulation still acts on the PGV, so that it can still be used under the condition of asynchronous networking of the Yunnan power grid. The advantages of the power closed loop of the speed regulator are summarized as follows: 1) The power regulation process has a smooth and soft curve, and the transition process of the regulation can be adjusted according to the artificially set model, which can effectively reduce the frequent occurrence of reverse regulation. 2) The power closed loop power regulation is more accurate and more stable. 3) In the power closed loop mode, the speed regulator's primary frequency regulation and power regulation occur on one device, so the control model can effectively achieve a balance between the two. There will be no conflict between the two, which will lead to a frequency regulation assessment. In power mode, the speed regulator's adjustment response target is the active power of the unit, which is consistent with the target object of the grid assessment and concern. 4) For power stations with long water diversion systems, one pipe with multiple units, large output interference between units, large pressure fluctuations, and large water flow inertia, the power closed loop can stabilize the load at the target value in a timely manner to ensure that it does not deviate from the given load curve. 5) Since the power control adopts the classic PID regulation, the control methods and control improvements based on this are diverse. In addition, the speed regulator directly controls the power, and the control strategy of the speed regulator is flexible and diverse, and the control strategy can be optimized for power regulation in a targeted manner.

[0108] Furthermore, the process of the ramp processing of the given power in step S11 specifically includes the following steps:

[0109] Step S111: Based on historical data and the current system status, a neural network is used to predict the changing trend of power demand, and the prediction result is used to dynamically adjust the slope of the ramp; in the process of ramp generation, an adaptive smoothing filter is used to adjust the filter parameters in real time according to the dynamic response characteristics of the system; the power given signal is divided into multiple ramp segments, and each segment is set with a different slope according to the current load characteristics and dynamic response capabilities of the system;

[0110] Step S112: Use the real-time collected system status data (such as power demand, load changes, and environmental parameters, etc.) to accurately predict future power demand; combine the feedforward prediction signal with the PID feedback control to form a dynamic collaborative optimization mechanism, the feedforward signal is used to adjust the system in advance; the PID feedback control is used to correct the error in the feedforward prediction; according to the real-time operating status of the system, dynamically adjust the weight of the feedforward signal and the feedback signal;

[0111] Step S113: Through the online performance evaluation mechanism, the key indicators such as the response time, overshoot, and stability of the system are monitored in real time; according to the evaluation results, the parameters of the ramp generator and the feedforward prediction model are dynamically adjusted; the system's response speed, stability, energy consumption and other objectives are incorporated into the multi-objective optimization framework; and the optimal balance point between multiple objectives is found through the genetic algorithm.

[0112] Preferably, step S111 of this embodiment can accurately predict the changing trend of power demand by analyzing historical data and current system status through neural network, so as to make adjustments in advance to avoid system overload or power shortage; adjust the filter parameters in real time according to the dynamic response characteristics of the system to ensure smooth transition during the slope generation process and reduce system oscillation and overshoot; set different slopes according to the current load characteristics and dynamic response capabilities of the system, so that the slope generation is more flexible and accurate and adapts to different system states. Significance achieved: Through accurate power demand prediction and smooth slope generation, the stability and reliability of the system can be effectively improved, and system failures caused by power fluctuations can be reduced; by dynamically adjusting the slope slope, it is possible to reduce unnecessary energy consumption as much as possible while meeting system requirements and improve energy efficiency. Step S112 uses the system status data collected in real time to accurately predict future power demand and make adjustments in advance to ensure the smooth operation of the system; the feedforward prediction signal is combined with the PID feedback control to form a dynamic collaborative optimization mechanism. The feedforward signal is used to adjust the system in advance, and the PID feedback control is used to correct the error in the feedforward prediction to ensure accurate control of the system; according to the real-time operating status of the system, the weights of the feedforward signal and the feedback signal are dynamically adjusted to ensure that the system can obtain the best control effect under different states. Significance achieved: Through the combination of feedforward prediction and PID feedback control, the control accuracy of the system can be significantly improved, the error can be reduced, and the accuracy of the system operation can be ensured; the signal weight can be dynamically adjusted so that the system can obtain the best control effect under different operating conditions, and the adaptability and flexibility of the system can be enhanced; through the online performance evaluation mechanism, the key indicators such as the response time, overshoot, stability, etc. of the system can be monitored in real time to ensure that the system is always in the best state during operation; according to the evaluation results, the parameters of the ramp generator and the feedforward prediction model can be dynamically adjusted to ensure that the system can obtain the best control effect under different operating conditions; the system's response speed, stability and energy consumption and other goals are included in the multi-objective optimization framework, and the optimal balance point between multiple goals is found through genetic algorithms to ensure that the system can achieve the best performance in multiple aspects. Significance achieved: Through real-time monitoring and dynamic adjustment, the overall performance of the system can be significantly improved, ensuring that the system is always in the best state during operation; through the multi-objective optimization framework, the optimal balance point can be found between multiple goals such as response speed, stability and energy consumption to ensure that the system can achieve the best performance in multiple aspects.

[0113] In summary, each step of this embodiment plays a key technical effect in the ramp processing process of given power, and through these effects, it achieves important significance such as improving system stability, control accuracy and optimizing energy consumption. Through the above innovative methods, the ramp processing and feedforward control process of given power can not only achieve smooth transition and rapid response of signals, but also significantly improve the stability and anti-interference ability of the system; the control strategy based on dynamic adaptation and intelligent prediction provides a new solution for the high-performance operation of complex industrial control systems.

[0114] Furthermore, the filtering process of the power feedback in step S14 specifically includes the following steps:

[0115] Step S141: Adopting an adaptive filtering strategy based on a time window, by dynamically adjusting the time constant of the filter, real-time performance is maintained under different working conditions, and the time constant of the filter is adaptively adjusted according to the frequency characteristics of power fluctuations;

[0116] Step S142: decompose the power signal into different frequency scales by wavelet transform, and filter out high-frequency clutter and burrs; use Kalman filter to dynamically predict the trend of power fluctuation according to the historical data and current data of power feedback, and perform smoothing on it. By combining wavelet transform and Kalman filter, high-frequency noise is filtered out while accurately tracking the main line of power fluctuation;

[0117] Step S143: By real-time monitoring of the filtered power feedback signal, the matching degree between the filtered power feedback signal and the actual power fluctuation is evaluated, and the filtering parameters are dynamically adjusted according to the evaluation result.

[0118] Preferably, step S141 of this embodiment adopts an adaptive filtering strategy based on a time window, and by dynamically adjusting the time constant of the filter, ensures that the filter can quickly respond to power changes under different working conditions and maintain the real-time performance of the system; according to the frequency characteristics of power fluctuations, the filter can automatically adjust its time constant, thereby achieving the best filtering effect in different frequency ranges. The significance achieved: in the case of large power fluctuations, the adaptive filtering strategy can quickly adjust the filter parameters to ensure that the system can respond to power changes in a timely manner and avoid system instability caused by filtering delays; through adaptive adjustment, the filter can maintain the best filtering effect under different working conditions, effectively remove noise, and retain the main features of the power signal. Step S142 combines wavelet transform and Kalman filtering. The wavelet transform can decompose the power signal into different frequency scales, filter out high-frequency clutter and burrs, and ensure the purity of the signal; the Kalman filter dynamically predicts the trend of power fluctuations based on the historical data and current data of power feedback, and smoothes the signal to effectively remove random noise; combined with wavelet transform and Kalman filtering, it can accurately track the main line of power fluctuations while filtering high-frequency noise, ensuring the continuity and stability of the signal. Significance achieved: Through the combination of wavelet transform and Kalman filtering, high-frequency noise and random fluctuations can be effectively removed, and the purity and reliability of the power feedback signal can be improved; the dynamic prediction function of the Kalman filter enables the system to better predict the trend of power fluctuations and provide accurate reference for subsequent control and adjustment. Step S143 real-time monitoring and dynamic adjustment, through real-time monitoring of the filtered power feedback signal, can timely evaluate its matching degree with the actual power fluctuation to ensure the accuracy of the filtering effect; according to the evaluation results, dynamically adjust the filtering parameters to ensure that the filter always maintains the best filtering effect under different working conditions. Significance achieved: Through real-time monitoring and dynamic adjustment, it can ensure that the filter always maintains the best filtering effect under different working conditions, improve the accuracy and reliability of filtering; dynamically adjust the filtering parameters so that the system can better adapt to different power fluctuations and enhance the system's adaptability and stability.

[0119] In summary, the filtering process of this embodiment realizes efficient filtering of the power feedback signal through the combination of adaptive filtering strategy, wavelet transform and Kalman filtering, as well as real-time monitoring and dynamic adjustment. Each step jointly improves the real-time performance, signal quality and adaptability of the system, and ensures the accuracy and stability of the power feedback signal. Through the above-mentioned hierarchical filtering technology process, not only the real-time and accuracy of power feedback can be guaranteed, but also clutter and burrs can be effectively filtered out, and the "main line" of power fluctuations can be accurately extracted, thereby providing reliable power feedback support for the stable control of the speed regulator.

[0120] Furthermore, the power regulation strategy of the speed regulator in step S1 specifically includes the following steps:

[0121] Step S15: The speed regulator receives the power mode switch-on / off signal, the power setting mode selection signal, the power regulation enable signal, and the 4-20mA power setting analog signal issued by the monitoring system; the speed regulator analyzes and determines the current working mode and power regulation target;

[0122] Step S16: In the power closed-loop mode, the speed governor automatically performs the primary frequency regulation and the secondary frequency regulation functions, and cancels the original frequency regulation blocking strategy of the monitoring system; the speed governor dynamically adjusts the output power according to the received power given signal to ensure that the unit response and regulation performance are optimal;

[0123] Step S17: The speed regulator feeds back the current power mode state and the 4-20mA power setting analog signal to the monitoring system, so that the monitoring system can compare and confirm the power setting, thereby realizing the handshake mechanism between the speed regulator and the monitoring system;

[0124] Step S18: The monitoring system optimizes the AGC setting dead zone, adjustment dead zone, allocation strategy and frequency modulation locking logic according to the adjustment result of the speed regulator to improve the response speed and adjustment accuracy of the AGC; at the same time, the monitoring system sends a load given signal to the speed regulator according to the AGC load distribution principle.

[0125] Preferably, in step S15 of this embodiment, the speed regulator can accurately receive and analyze various signals from the monitoring system, including power mode activation and deactivation signals, power setting mode selection signals, power regulation enable signals, and 4-20mA power setting analog signals. Through these signals, the speed regulator can determine the current working mode and power regulation target. Significance: It ensures that the speed regulator can respond quickly according to the instructions of the monitoring system, provides accurate basic data for power regulation, and ensures the accuracy and real-time performance of the regulation. In step S16, in the power closed-loop mode, the speed regulator automatically performs the primary and secondary frequency regulation functions, and cancels the original frequency regulation locking strategy of the monitoring system. The speed regulator dynamically adjusts the output power according to the received power setting signal to ensure that the unit response and regulation performance are optimal. Significance: Through the automatic frequency regulation function, the frequency response of the unit is optimized, and the stability and regulation accuracy of the system are improved; at the same time, the dynamic adjustment of the output power ensures that the unit can respond quickly to external demands and improve the overall operating efficiency. Step S17: The speed regulator feeds back the current power mode state and the 4-20mA power setting analog signal to the monitoring system, so that the monitoring system can compare and confirm the power setting, and realize the handshake mechanism between the speed regulator and the monitoring system. Significance: Through the feedback mechanism, the information synchronization between the speed regulator and the monitoring system is ensured, and the reliability and consistency of the system are enhanced; the monitoring system can make real-time adjustments based on the feedback information to further optimize the power regulation strategy. Step S18: The monitoring system optimizes the AGC setting dead zone, adjustment dead zone, allocation strategy and frequency modulation locking logic according to the adjustment results of the speed regulator, and improves the response speed and adjustment accuracy of the AGC. At the same time, the monitoring system sends a load setting signal to the speed regulator according to the AGC load distribution principle. Significance: By optimizing the various parameters of the AGC, the overall response speed and adjustment accuracy of the system are improved, and the rationality and efficiency of the load distribution are ensured; at the same time, the monitoring system sends signals according to the AGC load distribution principle, which further enhances the coordination and control accuracy of the system (the principle is shown in the attached figure). Figure 5 and attached Figure 6 ).

[0126] In summary, this embodiment ensures that the speed regulator can achieve efficient, stable and precise control during the power regulation process through precise signal reception, dynamic regulation, information feedback and strategy optimization, thereby improving the operating efficiency and reliability of the entire system.

[0127] Furthermore, the process of coordinating the frequency modulation and power regulation in step S16 specifically includes the following steps:

[0128] Step S161: The speed regulator adopts the power mode, and the adjustment object of the primary frequency regulation is the active power of the unit; when the primary frequency regulation is in a steady load state and there is no external load setting, the adjustment amount of the primary frequency regulation will not be pulled back by the power regulation; when there is an external load regulation instruction, the speed regulator will give priority to responding to the power regulation request regardless of whether the primary frequency regulation is in action;

[0129] Step S162: The load increase and decrease caused by the primary frequency regulation is superimposed according to the principle shown in the drawing; steady-state load regulation, when the load is in a steady state, the adjustment amount of the primary frequency regulation should be independent of the power regulation; dynamic load regulation, when there is an external load regulation instruction, the speed regulator should respond to the power regulation first;

[0130] Step S163: The speed regulator calculates the power deviation in real time, and quickly adjusts the guide vane opening according to the deviation value to compensate for the delay caused by the mechanical dead zone and the electrical dead zone; dynamically adjusts the action amount of the primary frequency regulation according to the change of the grid frequency, monitors the action amount of the primary frequency regulation in real time, and optimizes the frequency regulation strategy according to historical data;

[0131] Step S164: monitor the action amount of primary frequency regulation in real time and compare it with the theoretical calculated amount; automatically adjust the frequency regulation strategy according to the power grid assessment standard.

[0132] Preferably, in step S161 of this embodiment, the speed regulator adopts the power mode to ensure that the adjustment object of the primary frequency regulation is the active power of the unit; when the load is in a steady state and there is no external load setting, the adjustment amount of the primary frequency regulation will not be pulled back by the power regulation, ensuring the independence and accuracy of the frequency regulation; when there is an external load regulation instruction, the speed regulator responds to the power regulation requirement first, ensuring the rapid response capability of the system. The significance achieved: ensuring that the unit can maintain stable power output under different operating conditions, and can respond quickly when the external load changes, thereby improving the stability and reliability of the power grid. The load increase and decrease caused by the primary frequency regulation in step S162 is superimposed according to the principle shown in the drawing, ensuring the accuracy and consistency of the load regulation; in steady-state load regulation, the adjustment amount of the primary frequency regulation is independent of the power regulation, ensuring the independence of the frequency regulation; in dynamic load regulation, the speed regulator responds to the power regulation first, ensuring the rapid response capability of the system. The significance achieved: ensuring that the unit can maintain accurate power regulation under different load conditions, and can respond quickly when the external load changes, thereby improving the regulation accuracy and response speed of the power grid. In step S163, the speed regulator calculates the power deviation in real time, and quickly adjusts the guide vane opening according to the deviation value to compensate for the delay caused by the mechanical dead zone and the electrical dead zone, ensuring the rapidity and accuracy of the frequency modulation; according to the change of the grid frequency, the action amount of the primary frequency modulation is dynamically adjusted, the action amount of the primary frequency modulation is monitored in real time, and the frequency modulation strategy is optimized according to the historical data, ensuring the intelligence and adaptability of the frequency modulation. The significance achieved: It ensures that the unit can quickly adjust the power output when the grid frequency changes, and improves the frequency stability and regulation efficiency of the grid; at the same time, through the optimization of historical data, the intelligence and adaptability of the frequency modulation strategy are improved. Step S164 monitors the action amount of the primary frequency modulation in real time and compares it with the theoretical calculation amount to ensure the accuracy and consistency of the frequency modulation; according to the grid assessment standard, the frequency modulation strategy is automatically adjusted to ensure the compliance and efficiency of the frequency modulation. The significance achieved: It ensures that the unit can maintain a compliant frequency modulation strategy under the grid assessment standard, and improves the assessment pass rate and operation efficiency of the grid (for specific principles, please refer to the attached Figure 7 and attached Figure 8 ).

[0133] To sum up, the coordination process of primary frequency regulation and power regulation in this embodiment ensures the stable operation and efficient regulation of the power grid, improves the stability, regulation accuracy and response speed of the power grid, and at the same time improves the operation efficiency and assessment pass rate of the power grid through intelligent frequency regulation strategies; through innovative frequency regulation methods, efficient coordination of primary frequency regulation and power regulation is achieved, the frequency regulation response speed is improved, the problem of insufficient integrated electricity is compensated, and the power plant is avoided from being assessed by the power grid, ultimately improving the precise regulation capability of the power grid system frequency.

[0134] Furthermore, the process of switching the speed regulator power mode and the opening mode in step S17 specifically includes the following steps:

[0135] Step S171: The speed regulator is in normal operation and uses the power mode for regulation. The speed regulator directly responds to the power command. The opening closed loop is used as a backup plan for the power mode. When the power mode is abnormal, the basic load adjustment capability is maintained.

[0136] Step S172: When the power closed-loop regulation system detects a fault or anomaly, the fault type and time are automatically identified and recorded; after the fault is identified, the speed regulator automatically switches to the opening mode; in some cases, the operator chooses to manually switch to the opening mode according to the actual situation;

[0137] Step S173: When the fault that causes the automatic exit of the power closed loop is repaired, the fault recovery state is automatically detected; no matter whether the fault recovery is completed automatically or manually intervened, it will not automatically switch from the opening mode to the power mode; after the operator confirms, the speed regulator switches from the opening mode to the power mode; after the mode switching is completed, the operating status of the speed regulator is monitored in real time.

[0138] Preferably, in step S171 of this embodiment, the power mode is dominant and the opening mode is standby. The speed regulator directly responds to the power command under normal operation to ensure that the active power output of the unit is accurately matched with the grid demand, and realizes efficient and accurate power regulation; the opening closed loop is used as a backup solution to ensure that when the power mode is abnormal, the system can still maintain basic load adjustment capabilities to avoid system failure caused by a single mode failure. Significance achieved: Through the dual-mode design, the adaptability and reliability of the speed regulator under different working conditions are enhanced to ensure that the unit can maintain basic operating capabilities under any circumstances; the high-precision adjustment of the power mode and the backup guarantee of the opening mode jointly improve the operating efficiency and stability of the system and reduce the operating fluctuations caused by mode switching. Step S172 Fault detection and mode switching, the system can quickly and accurately identify the fault or abnormality of the power closed-loop regulation system, and record the fault type and time, providing data support for subsequent fault analysis and processing; after fault identification, the speed regulator automatically switches to the opening mode to ensure that the system can still maintain basic load adjustment functions under fault conditions, avoiding system shutdown or loss of control due to faults; in certain cases, the operator can choose to manually switch to the opening mode according to actual conditions, which enhances the flexibility of the system and its ability to cope with complex working conditions. Significance achieved: Through both automatic and manual switching methods, the system can respond quickly when a fault occurs, ensuring that the unit can still maintain basic operating capabilities under fault conditions and reducing the impact of faults on system operations; the manual intervention switching mechanism provides operators with more operating options, allowing the system to flexibly respond under complex or special conditions, improving the system's adaptability and ease of operation. Step S173 fault recovery and mode recovery can automatically detect the fault recovery state to ensure that the system can promptly identify and prepare to recover to normal operation after the fault is repaired; after the fault is recovered, it will not automatically switch back to the power mode from the opening mode, but requires the operator to make a second confirmation to ensure the safety and accuracy of the switch; after the mode switch is completed, the system monitors the operating status of the speed regulator in real time to ensure the adjustment accuracy and stability in the power mode, and optimizes and adjusts through data feedback to improve the response speed and adjustment accuracy of the system. Significance achieved: Through the manual secondary confirmation mechanism, the possible misoperation or system instability caused by automatic switching is avoided, and the safety and reliability of mode switching are ensured; the real-time monitoring and optimization adjustment mechanism ensures that the system can operate stably after returning to the power mode, reduces the operation fluctuations caused by mode switching, and improves the overall stability of the system.

[0139] In summary, through the above steps, the switching process of the speed regulator between the power mode and the opening mode in this embodiment not only achieves technical efficiency and flexibility, but also improves the reliability, stability and operational safety of the system in actual operation. Each step together builds an efficient, reliable and flexible speed regulation system, ensuring the stable operation and efficient regulation of the unit under various operating conditions. Through this well-defined, rigorous and flexible switching process, the speed regulator of the Ahai Power Plant unit achieves seamless switching between the power mode and the opening mode, ensuring the stable operation and efficient regulation of the system under various operating conditions. This design not only improves the reliability of the system, but also further enhances the safety and accuracy of the operation through the manual secondary confirmation mechanism.

[0140] Furthermore, the variable parameter control process of the speed regulator in step S1 specifically includes the following steps:

[0141] Get the current speed regulator parameter value and determine whether the current parameter value is within the preset reasonable range; if it is not within the range, it needs to be adjusted;

[0142] If the parameter value is within the range, it is further determined whether it meets the set judgment standard; if not, the parameter needs to be adjusted; according to the judgment result, the speed regulator parameter is adjusted to change in the direction of meeting the standard or target value;

[0143] Repeat the above steps until the speed regulator parameters are stable within a reasonable range and meet the judgment criteria.

[0144] Preferably, the current speed regulator parameter value is obtained in this embodiment and compared with a preset reasonable range. Significance: Ensure that the speed regulator parameters are always within a controllable and safe range to avoid system loss of control or damage due to parameters exceeding the range. This is the basis of variable parameter control and provides a basis for subsequent adjustments. Under the premise that the parameter value is reasonable, further determine whether the set performance standard or target value is met. If not, adjust the parameters according to the judgment result so that they change in the direction of meeting the standard or target value. Significance: Realize fine adjustment of the speed regulator parameters to optimize system performance; by continuously approaching the target value, improve the key performance indicators such as the response speed and stability of the system to meet user needs. Repeat steps S19 and S20 until the speed regulator parameters are stable within a reasonable range and meet the judgment criteria. Significance: Ensure that the final adjustment result of the speed regulator parameters is stable and reliable; eliminate parameter fluctuations through iterative optimization, so that the system enters a stable working state, and provides guarantee for normal operation.

[0145] In summary, this embodiment realizes automatic adjustment and optimization of the speed regulator parameters, so that the system performance reaches the best and meets the user needs; improves the automation and intelligence level of the system, reduces manual intervention, and improves work efficiency; enhances the adaptability and robustness of the system so that it can cope with various complex working conditions and improves the reliability and stability of the system; and provides technical support for the realization of advanced manufacturing modes such as intelligent manufacturing and Industry 4.0.

[0146] Table 1 Speed ​​governor variable parameter range

[0147]

[0148] Furthermore, if Fig.10 As shown, the process of identifying the operating status and potential faults of the speed regulator in step S2 specifically includes the following steps:

[0149] Step S21: The sensors installed in various parts of the speed control system will monitor various physical signals in real time, such as pressure, temperature and vibration, and convert the physical signals into electrical signals, which are transmitted to the industrial computer through the speed control cabinet; after receiving the electrical signals, the industrial computer performs preliminary data processing, including filtering, amplification and analog-to-digital conversion, and analyzes and extracts characteristic parameters reflecting the operating status of the speed control system, such as frequency, amplitude and waveform;

[0150] Step S22: The extracted characteristic parameters are monitored in real time and compared with the preset normal operating range; if some parameters exceed the normal range, an alarm is triggered to indicate that there may be a fault; when an abnormality is detected, a fault recording is automatically performed, that is, all relevant data before and after the fault occurs are recorded, and the data is stored in the industrial computer and uploaded to the background comprehensive diagnosis system regularly;

[0151] Step S23: After receiving these data, the background comprehensive diagnosis system analyzes them, including the identification of fault types and the speculation of fault causes; generates status inspection logs and status development trend reports; provides auxiliary decision-making suggestions based on the analysis results, such as whether status maintenance is needed and when to perform the maintenance; displays them through the human-machine interface and uploads them to the background comprehensive diagnosis system for reference by operation and maintenance personnel; all generated status data, analysis results and diagnostic reports are shared through the network, allowing local or remote user terminals to query and download data through the browser.

[0152] Preferably, in step S21 of this embodiment, data collection and feature extraction, the sensor can collect physical signals from various parts of the speed control system in real time to ensure the timeliness and accuracy of the data; the physical signal is converted into an electrical signal to facilitate subsequent digital processing and analysis; the industrial computer filters, amplifies and performs analog-to-digital conversion on the electrical signal to remove noise and interference and improve the reliability of the data; characteristic parameters reflecting the operating status of the speed control system, such as frequency, amplitude and waveform, are extracted through analysis to provide basic data for fault diagnosis. Significance: Through real-time monitoring and data processing, the abnormal state of the speed control system can be discovered in time, potential faults can be warned in advance, and sudden accidents can be avoided; the extracted characteristic parameters provide a reliable data basis for subsequent fault diagnosis and status analysis, ensuring the accuracy of the analysis results. Step S22 real-time monitoring and fault recording, compare the extracted characteristic parameters with the preset normal operating range in real time, and quickly identify abnormal conditions; when the parameters exceed the normal range, the system automatically triggers an alarm to remind the operation and maintenance personnel to pay attention to potential faults; when an abnormality is detected, the system automatically records all relevant data before and after the fault occurs, providing detailed historical data for subsequent fault analysis; abnormal data is stored in the industrial computer and regularly uploaded to the background comprehensive diagnosis system to ensure data integrity and traceability. Significance: Through real-time monitoring and alarm mechanisms, it is possible to quickly respond to potential faults and reduce the impact of faults on system operation; the fault recording function provides detailed historical data for fault analysis, which helps to accurately determine the cause of the fault and formulate a repair plan; the regularly uploaded data provides rich historical data for the background comprehensive diagnosis system, which is helpful for long-term status monitoring and trend analysis. Step S23: Comprehensive diagnosis and decision support. The background comprehensive diagnosis system can conduct in-depth analysis of the uploaded data, identify the fault type and infer the fault cause. The system generates a status check log and a status development trend report to help the operation and maintenance personnel fully understand the health of the speed control system. Based on the analysis results, the system provides auxiliary decision suggestions, such as whether to perform status maintenance and when to perform maintenance, etc., to help the operation and maintenance personnel formulate a reasonable maintenance plan. All generated status data, analysis results and diagnostic reports are shared through the network, allowing local or remote user terminals to query and download data, perform custom analysis or specify specific operations. Significance: Through comprehensive diagnosis, the fault type and cause can be accurately identified, and the efficiency and accuracy of fault handling can be improved. The status check log and trend report help the operation and maintenance personnel fully understand the operating status of the speed control system and formulate scientific and reasonable maintenance strategies. The auxiliary decision suggestions provided help the operation and maintenance personnel optimize the maintenance plan, reduce unnecessary downtime, and improve the reliability and operating efficiency of the system. The data sharing function enables remote users to participate in fault analysis and maintenance decisions, and improve the flexibility and response speed of operation and maintenance.

[0153] In summary, the operating status and potential faults of the speed control system of this embodiment can be effectively identified and monitored to ensure the stable operation and timely maintenance of the turbine unit. Each step complements each other to form a complete fault diagnosis and status management system, which provides a strong guarantee for the safe operation of the speed control system.

[0154] Furthermore, if Fig.11 As shown, the process of performing hierarchical analysis on the speed regulator in step S3 specifically includes the following steps:

[0155] Step S31: determine the top event, intermediate event and bottom event, define the logical relationship between the events: such as logic gates such as "AND" and "OR", and construct the hierarchical structure of the fault tree; the top event is the most undesirable fault event of the system; the intermediate event and bottom event analyze the direct cause and root cause of the top event;

[0156] Step S32: convert the fault tree into a standard form, remove redundant events and logic gates; decompose the fault tree into multiple independent modules; find all minimum cut sets, that is, the minimum combination of all bottom events that cause the top event to occur; analyze the failure mode of each minimum cut set: determine which combination of bottom events will cause the top event to occur;

[0157] Step S33: Calculate the failure probability of each bottom event and intermediate event, and evaluate the influence of each bottom event on the occurrence of the top event; calculate the overall occurrence probability of the top event based on the failure probability and logical relationship of the bottom events; start from the top event, search the nodes in the fault tree layer by layer; call the symptom extraction function to obtain the symptoms related to the current node, and select the decision rule through pattern matching; calculate the credibility of the node, and compare it with the decision threshold to determine whether the node has failed; mark and classify the abnormal nodes and normal nodes respectively;

[0158] Step S34: when forward reasoning cannot directly determine certain sub-nodes, reverse reasoning is used to verify whether they have failed; based on the reverse verification results, sub-nodes that have not failed are excluded; combined with the critical importance of the bottom event, the probability of occurrence of each faulty node is calculated; the diagnosis results are sorted from large to small according to the probability; the diagnosis results are output through the human-computer interaction module, and the fault name, characteristics and probability of occurrence are displayed;

[0159] Step S35: Combine expert knowledge with real-time monitoring data to find the best match between actual data and simulation data; when the matching error is within the allowable range, continue searching along the path until the basic event is found; integrate forward reasoning and reverse verification to form a hybrid reasoning process.

[0160] Preferably, step S31 of this embodiment determines the top event, the intermediate event and the bottom event, and defines the logical relationship; constructs a clear fault tree hierarchy to clarify the cause-effect relationship of the fault; defines the correlation between events through logic gates (such as "and" and "or") to facilitate subsequent analysis. Significance: provides a clear logical framework for fault analysis to help quickly locate the source of the fault; clarifies the most undesirable fault events (top events) in the system and their direct and root causes, and provides direction for troubleshooting. Step S32 normalizes, simplifies and decomposes the fault tree into modules; converts the fault tree into a standard form, removes redundant events and logic gates, and reduces computational complexity; decomposes the fault tree into multiple independent modules for step-by-step analysis; finds all minimum cut sets and identifies all fault combinations that lead to the top event. Significance: optimizes the fault tree structure, improves analysis efficiency, and reduces computational costs; comprehensively identifies the potential causes of system failures through minimum cut set analysis to provide a basis for troubleshooting; modular analysis facilitates targeted processing of fault problems in complex systems. Step S33: Quantitative analysis and forward reasoning, calculate the failure probability of bottom events and intermediate events, and quantify the failure risk; evaluate the impact of bottom events on top events, and identify key fault sources; through forward reasoning, search the fault tree nodes layer by layer to quickly locate the fault; call the symptom extraction function, combine pattern matching and threshold comparison to determine whether the node has failed. Significance: Provide data support for system reliability assessment and help formulate targeted maintenance strategies; through forward reasoning, quickly locate the faulty nodes in the system and improve the efficiency of troubleshooting; mark abnormal nodes and normal nodes to provide clear fault classification for subsequent analysis. Step S34: Reverse verification and troubleshooting, when forward reasoning cannot directly determine whether certain sub-nodes have failed, reverse reasoning is used to verify whether they have failed; based on the reverse verification results, exclude sub-nodes that have not failed to improve diagnostic accuracy; combine the key importance of the bottom event to calculate the probability of occurrence of the faulty node and quantify the failure risk; sort the diagnosis results by probability to facilitate the priority handling of high-risk faults. Significance: Through reverse verification, misjudgment can be avoided and the accuracy of fault diagnosis can be improved; fault risks can be quantified to provide a scientific basis for maintenance decisions; diagnostic results can be output in a user-friendly manner to facilitate rapid response and fault handling. Step S35 combines data constraint propagation and hybrid reasoning, combining expert knowledge with real-time monitoring data to find the best match between actual data and simulation data; when the matching error is within the allowable range, continue searching along the path until the basic event is found; forward reasoning and reverse verification are integrated to form a hybrid reasoning process to improve diagnostic efficiency and accuracy. Significance: Through data constraint propagation, the fault tree search path is optimized, unnecessary calculations are reduced, and diagnostic efficiency is improved; hybrid reasoning combines the advantages of forward and reverse verification to comprehensively improve the accuracy and efficiency of fault diagnosis; it provides an efficient and reliable method for fault analysis of complex systems (the specific principle is in the attached figure). Fig.12 ).

[0161] In summary, the various steps of the hierarchical analysis of the speed regulator in this embodiment realize the construction, optimization, qualitative and quantitative analysis, and the combination of forward reasoning and reverse verification of the fault tree through technical means, and comprehensively identify and evaluate the fault risk of the speed regulator. Its significance lies in: quickly locating the fault source by optimizing the fault tree structure and hybrid reasoning; avoiding misjudgment and ensuring the reliability of the diagnosis results through reverse verification and data constraint propagation; providing a scientific basis for maintenance decisions through quantitative analysis and helping to formulate targeted maintenance strategies; outputting the diagnosis results in an intuitive way to facilitate rapid response and handling of faults. Through these steps, the fault diagnosis of the speed regulator is not only more efficient and accurate, but also provides strong support for the safe and reliable operation of the system. Fault tree diagnosis comprehensively identifies and evaluates the fault risk of the system by establishing a fault tree, standardized processing, qualitative and quantitative analysis, and combining forward reasoning with reverse verification. Each step is interconnected to form a complete fault diagnosis process, which provides a scientific basis for the safe and reliable operation of the system.

[0162] like Fig.13 As shown, this embodiment also provides an embodiment of an intelligent control system for a hydropower set speed governor. In this embodiment, the intelligent control system for a hydropower set speed governor is applied to the intelligent control method for a hydropower set speed governor in the above embodiment. The intelligent control system for a hydropower set speed governor includes a dynamic adjustment module 1, a state evaluation module 2, and a fault analysis module 3 that are electrically connected in sequence;

[0163] Among them, the dynamic adjustment module 1 is used to dynamically adjust the control parameters of the power closed loop and the opening closed loop by introducing variable structure and variable parameter control technology; real-time calculation and feedback control, the speed regulator responds to the power grid frequency regulation command; the state evaluation module 2 is used to collect the operating data of the key components of the speed regulator (such as servo valve, main pressure regulating valve, oil pressure device, etc.) in real time through sensors, including parameters such as temperature, pressure, vibration and flow; big data is used to store and preprocess the collected parameters, and artificial intelligence algorithms are used to extract features and recognize patterns of the data to identify the operating status and potential faults of the speed regulator; by constructing a state evaluation model, the health status of the speed regulator is scored in real time and an operating status report is generated; the fault analysis module 3 is used to construct a fault tree model based on the parameters of the speed regulator state monitoring, perform hierarchical analysis on the speed regulator, and locate the specific location and cause of the fault; combined with expert knowledge in the field of speed regulators, an expert system is constructed to verify and optimize the fault diagnosis results and generate targeted fault handling suggestions; according to the fault diagnosis results, the control parameters or operating mode of the speed regulator are automatically adjusted to achieve adaptive regulation.

[0164] Preferably, the dynamic adjustment module 1 of this embodiment adopts variable structure and variable parameter control technology to dynamically adjust the control parameters of the power closed loop and the opening closed loop to achieve a rapid response to the power grid frequency regulation command; real-time calculation and feedback control to ensure the control accuracy and stability of the speed regulator. Significance: Improve the response speed and accuracy of the speed regulator to the power grid frequency regulation command and enhance the stability of the power grid; optimize the operating performance of the speed regulator by dynamically adjusting the control parameters, reduce energy consumption, and extend the life of the equipment; lay the foundation for the intelligent control of the hydropower unit and improve the automation level of the system. The state evaluation module 2 collects the operating data of the key components of the speed regulator (such as servo valves, main pressure regulating valves, oil pressure devices, etc.) in real time through sensors, including parameters such as temperature, pressure, vibration and flow; uses big data technology to store and preprocess the collected parameters, and combines artificial intelligence algorithms for feature extraction and pattern recognition; builds a state evaluation model to score the health status of the speed regulator in real time and generate an operating status report. Significance: Comprehensive monitoring of the speed governor's operating status is achieved, potential faults are discovered in time, and faults are prevented from occurring; the accuracy and intelligence level of status assessment are improved through big data and artificial intelligence technology; the real-time generated operating status report provides a scientific basis for decision-making for operation and maintenance personnel, improving the efficiency of operation and maintenance; it provides data support for the health management of the speed governor and prolongs the service life of the equipment. Fault analysis module 3 builds a fault tree model based on the parameters of speed governor status monitoring, conducts hierarchical analysis of the speed governor, and locates the specific location and cause of the fault; combines expert knowledge in the field of speed governors to build an expert system to verify and optimize the fault diagnosis results; according to the fault diagnosis results, automatically adjust the control parameters or operating mode of the speed governor to achieve adaptive adjustment. Significance: Through the fault tree model and hierarchical analysis, the fault source can be quickly located, the efficiency and accuracy of fault diagnosis can be improved; combined with the expert system, the reliability of the fault diagnosis results can be ensured, and a scientific basis can be provided for fault handling; the control parameters or operating mode can be automatically adjusted to achieve adaptive adjustment, reduce manual intervention, and improve the intelligence level of the system; it provides an intelligent solution for the prevention and handling of speed governor faults, and improves the reliability and safety of the system.

[0165] In summary, the various modules of the intelligent control system of the hydropower unit speed governor of this embodiment realize comprehensive intelligent control, state monitoring and fault diagnosis of the speed governor through technical means, and its significance lies in: the dynamic adjustment module improves the response speed and control accuracy of the speed governor and enhances the stability of the power grid by optimizing the control parameters in real time; the state assessment module provides a scientific basis for the health management of the speed governor and extends the life of the equipment through real-time monitoring and intelligent analysis; the fault analysis module quickly locates the source of the fault through the fault tree model and expert system, and provides an intelligent solution for fault handling; through intelligent control, monitoring and diagnosis, the overall reliability and safety of the speed governor system are improved. Through the organic combination of these modules, the intelligent control system of the hydropower unit speed governor not only realizes the comprehensive intelligent management of the speed governor, but also provides strong support for the stable operation and efficient maintenance of the hydropower unit.

[0166] Furthermore, the status assessment module 2 specifically includes:

[0167] The human-machine interaction interface module is used to provide an intuitive and friendly operation interface, allowing operators to view the operating status, parameters and alarm information of the speed regulator and hydraulic device in real time; it supports functions such as data query, historical record playback and fault diagnosis result display;

[0168] Data communication module, used for data communication with the speed governor PCC and the speed governor oil pressure device PLC, to ensure that all parameters and operating data of the speed governor and oil pressure device can be transmitted to the online monitoring and self-diagnosis system in real time;

[0169] Data processing module, used to process the raw data collected from the speed governor and hydraulic device, including data cleaning, format conversion and data storage;

[0170] The data diagnosis module is used to perform intelligent diagnosis and analysis based on the data provided by the data processing module to identify abnormal operation and potential failures of the speed governor and oil pressure device; it provides a fault warning function to help operators discover and deal with potential failures in advance to avoid accidents;

[0171] The accident recall module is used to record key data and events during system operation. In particular, when a failure or accident occurs, it can trace back the operating status before and after the failure. It supports historical data query and analysis to help operators understand the cause of the failure and optimize equipment operation.

[0172] The fault alarm module is used to monitor the operating status of the speed governor and oil pressure device in real time, and send out an alarm signal in time when an abnormality or fault is detected; it supports multiple alarm modes, such as sound, light, interface prompts, etc., to ensure that operators can respond quickly and handle faults.

[0173] Preferably, these modules of this embodiment work together to ensure the safe and stable operation of the turbine governor system of the Ahai Power Plant, and improve the reliability and operation efficiency of the equipment through intelligent monitoring and diagnosis functions.

[0174] Furthermore, the data diagnosis module specifically includes:

[0175] The data call and processing submodule is used to effectively obtain various equipment basic data, real-time data, test data and other data reflecting the equipment health status indicators from the intelligent unified data platform. The original data of the analysis object obtained after the data call is filtered, converted, combined and processed according to the needs of remote monitoring and diagnosis, so that it becomes the state quantity data reflecting the health status of the equipment, providing complete information resources for monitoring and early warning, status evaluation, risk assessment and maintenance strategy. It can adopt regular automatic triggering mode (adjustable cycle), manual triggering mode and background automatic processing;

[0176] The monitoring and early warning submodule is used to monitor the changes of state quantity indicators in real time. For the degradation indicators that exceed the threshold range specified in the state evaluation guidelines and regulations, early warning information is transmitted to the production management and decision support system platform (automatically) according to different categories and levels, and the equipment state diagnosis module is started at the same time to assist in analyzing the fault location and cause;

[0177] The status evaluation submodule is used to analyze and evaluate the data of various indicators reflecting the health status of the equipment, and finally obtain the overall health status level of the equipment; it realizes automatic triggering and manual triggering functions, and flexibly configures and evaluates single or multiple equipment according to a variety of guidance methods;

[0178] The prediction and evaluation submodule is used to predict various types of continuous data using a time series algorithm, and to analyze and judge the predicted data using the expert system knowledge base to obtain the predicted status of the equipment;

[0179] The risk assessment submodule is used to identify potential internal defects and external threats of the equipment, analyze the degree of asset loss and the probability of threat occurrence after the equipment is threatened with failure, and derive the risk level of the equipment in the power grid through the risk assessment model; it can be triggered automatically or manually;

[0180] The fault diagnosis submodule is used to diagnose the possible fault causes and fault locations of equipment with abnormal status and above, and equipment with status monitoring alarms, and provide basis and explanations to guide fault handling and recovery. It needs to be coordinated and managed with the fault diagnosis software function of the front-end online monitoring equipment manufacturer to achieve functional unification, and at the same time, it is necessary to formulate relevant fault diagnosis guidelines for the equipment. The equipment failure mechanism is analyzed according to the equipment fault components, fault locations and fault phenomena, so as to form the equipment fault tree, and the equipment fault phenomena are analyzed and located according to the decision tree theory, so as to determine the equipment failure cause and maintenance method.

[0181] The maintenance strategy submodule is used to establish a two-dimensional relationship model between equipment status and equipment failure risk based on the equipment status evaluation results and comprehensively consider the risk assessment conclusions, comprehensively optimize the maintenance order, maintenance time and maintenance level arrangement of the turbine speed control system equipment to determine the equipment maintenance management plan, which can be triggered automatically or manually; establish standards for the status of turbine speed control system equipment, and establish a standard library for equipment status; establish information models such as equipment fault status identification, comprehensive equipment information management, maintenance risk analysis, spare parts management, and maintenance personnel management; establish an accurate cost estimation model; use equipment health status, reliability, availability and other benefit factors to evaluate the benefits of equipment before maintenance, as well as the benefits after adopting various possible maintenance strategies, and establish a benefit estimation model; by analyzing the impact of maintenance strategies on costs and benefits, study the selection model of four types of maintenance strategies: full replacement, overhaul, partial repair, and continued status maintenance (i.e., deferred repair);

[0182] Other functional sub-modules are used to have the ability to classify and manage relevant test data (collected by the condition maintenance decision support system) by profession, have advanced function interfaces and processing capabilities, and have (third-party) secondary development capabilities, such as predictive evaluation, data mining and other functions.

[0183] Preferably, the data calling and processing submodule of this embodiment obtains various types of equipment health status data from the intelligent unified data platform, and performs filtering, conversion, combination and other processing to generate state quantity data reflecting the health status of the equipment; supports regular automatic triggering, manual triggering and background automatic processing. Significance: Ensure the accuracy and integrity of the data, provide a reliable data basis for monitoring and early warning, status evaluation, risk assessment and maintenance strategy, and improve the intelligence and automation level of the system. The monitoring and early warning submodule monitors the changes in the state quantity indicators in real time. When the indicators exceed the threshold range, it automatically transmits early warning information to the production management and decision support system, and starts the equipment status diagnosis module. Significance: Through the real-time early warning function, the equipment degradation trend can be discovered in time, the fault expansion can be avoided, the safety and reliability of equipment operation can be improved, and the possibility of accidents can be reduced. The status evaluation submodule analyzes and evaluates the data of each indicator item of the equipment health status, obtains the overall health status level of the equipment, supports automatic and manual triggering, and flexibly configures the evaluation method; Significance: Through regular status evaluation, it helps operators to fully understand the operating status of the equipment, provide a scientific basis for equipment maintenance and overhaul, and optimize equipment management strategies. The prediction and evaluation submodule uses a time series algorithm to predict continuous data, and analyzes and judges through the expert system knowledge base to obtain the future status of the equipment. Significance: Through prediction and evaluation, the potential deterioration trend of the equipment can be discovered in advance, helping operators to formulate preventive maintenance plans and reduce the occurrence of sudden equipment failures. The risk assessment submodule identifies the internal defects and external threats of the equipment, analyzes the degree of asset loss and the probability of threat occurrence after the equipment fails, and obtains the risk level of the equipment in the power grid through the risk assessment model. Significance: Through risk assessment, it helps decision makers to fully understand the risk status of the equipment, reasonably allocate resources, optimize equipment maintenance strategies, and reduce economic losses and safety risks caused by equipment failure. The fault diagnosis submodule performs fault diagnosis on equipment with status evaluation of abnormal or above, analyzes the cause and location of the fault, and provides a basis and explanation to guide fault handling and recovery. Significance: Through fault diagnosis, the cause of the fault can be quickly located, the troubleshooting time can be reduced, the recovery efficiency of the equipment can be improved, and the stable operation of the system can be guaranteed. Based on the equipment status evaluation and risk assessment results, the maintenance strategy submodule establishes a two-dimensional relationship model between equipment status and failure risk, optimizes the maintenance sequence, time and level arrangement, and formulates a maintenance management plan. Significance: Through scientific maintenance strategies, optimize maintenance plans, reduce unnecessary maintenance times, reduce maintenance costs, and improve equipment reliability and availability. Other functional submodules have test data classification management, advanced functional interfaces and secondary development capabilities, and support prediction evaluation, data mining and other functions. Significance: Through open interfaces and secondary development capabilities, improve the scalability and flexibility of the system, meet the needs of different scenarios, and enhance the adaptability and intelligence level of the system (for specific principles, refer to the attached Fig.14 ).

[0184] In summary, the submodules of this embodiment together constitute an intelligent data diagnosis system, which realizes all-round monitoring and management of the health status of the equipment through functions such as data collection, processing, monitoring, early warning, evaluation, prediction, risk assessment, fault diagnosis and maintenance strategy. The system not only improves the operating efficiency and reliability of the equipment, but also optimizes the maintenance strategy of the equipment through intelligent analysis and decision support, reduces the possibility of failure, and ensures the safe and stable operation of the equipment. At the same time, the openness and scalability of the system provide a good foundation for future functional upgrades and technological innovations, and enhance the long-term value and application potential of the system.

[0185] Furthermore, the fault analysis module 3 specifically includes:

[0186] Fault tree management and maintenance submodule, used to add, modify and delete nodes in the fault tree, as well as to correct the probability of occurrence of the bottom event;

[0187] The fault tree analysis submodule is used to complete the qualitative and quantitative analysis commonly used in fault tree diagnosis, to display fault information and provide assistance for fault diagnosis;

[0188] The fault diagnosis submodule is used to perform fault diagnosis from top to bottom according to the tree structure to obtain the diagnosis result;

[0189] The human-computer interaction submodule is used to provide interfaces for management and maintenance, analysis and display, diagnosis results, and result feedback.

[0190] Preferably, the fault tree management and maintenance submodule of this embodiment supports the addition, modification and deletion of nodes in the fault tree, as well as the correction of the probability of occurrence of the bottom event, to ensure the accuracy of the structure and data of the fault tree. Significance: Through the flexible fault tree management and maintenance function, the fault tree structure can be dynamically adjusted according to the actual situation of the equipment, ensuring the real-time and accuracy of the fault tree, and providing a reliable basis for fault analysis and diagnosis. The fault tree analysis submodule provides qualitative and quantitative analysis functions of the fault tree, displays fault information, and provides assistance for fault diagnosis. Significance: Through qualitative and quantitative analysis, the causes and effects of the fault can be fully understood, helping operators to quickly locate the cause of the fault, improve the efficiency and accuracy of fault diagnosis, and reduce the time for troubleshooting. The fault diagnosis submodule performs fault diagnosis from top to bottom according to the tree structure, and finally obtains the diagnosis result. Significance: Through the systematic fault diagnosis process, the fault location and cause can be quickly located, the possibility of human misjudgment can be reduced, the efficiency of fault handling can be improved, and the stable operation of the equipment can be guaranteed. The human-computer interaction submodule provides interfaces for management and maintenance, analysis and display, diagnosis results, and result feedback, ensuring that users can operate and interact conveniently. Significance: Through a friendly human-computer interaction interface, the system's usability and user experience are improved, allowing operators to quickly master the system's functions, reduce operational complexity, and improve work efficiency (for specific principles, refer to the attached Fig.15 ).

[0191] In summary, the fault analysis module 3 of this embodiment constructs a systematic fault diagnosis platform through the management, maintenance, analysis, diagnosis and interaction functions of the fault tree. This module can not only help operators quickly locate the cause of the fault and improve the efficiency of fault handling, but also ensure the real-time and accuracy of the fault tree through flexible management and maintenance functions. At the same time, the friendly human-computer interaction interface improves the ease of use of the system, allowing operators to diagnose and handle faults more efficiently. In general, the fault analysis module 3 is of great significance in improving the efficiency and accuracy of equipment fault diagnosis, can effectively reduce the downtime and economic losses caused by equipment failures, ensure the stable operation of equipment, and improve the intelligent level of equipment management.

[0192] According to the common fault types of the speed control system, this embodiment divides the speed control system operation fault tree into speed control system body fault, speed control system performance abnormality and oil pressure device operation fault. The cause and effect levels of the main faults are as follows: Fig.16 The fault tree structures of other components are shown in Figures 17 to 22 As shown. The fault judgment of guide vane and frequency measurement is shown in Fig.17 , see the main pressure distribution valve fault judgment Fig.18 , PCC controller and integrated control function block fault judgment see Fig.19 , see the fault judgment of the emergency pressure distribution valve and the staged closing valve Fig. 20, oil pressure device oil level oil pressure fault judgment see Fig.21 , oil temperature of oil pressure device and frequent pump start-up fault judgment see Fig. 22 , see the fault judgment of startup failure and grid connection failure Fig.23 , the judgment of abnormal load regulation in power generation condition is shown in Fig.24 ;

[0193] Mechanical faults mainly include proportional servo valve failure, main distribution blockage, water guide mechanism blockage, sensor slider detachment, main distribution zero point drift and oil filter blockage. The fault tree is as follows Figures 25 to 27 shown.

[0194] Electrical faults mainly refer to the failure of an electrical component or sensor. The fault tree is as follows: Fig.28 As shown. Similarly, for other faults, such as main distribution stuck, main distribution twitching, feedback fault, oil quality monitoring, etc., different alarm thresholds are set after analyzing and comparing the servo valve, main distribution, relay displacement and other links, and a decision is given after diagnosis. The solenoid valve fault diagnosis module grasps the valve working position by comparing the valve core position switch, and then obtains the coil current through the voltage across the resistor connected in series on the coil, calculates the coil resistance, and thus infers whether the solenoid valve is stuck and whether the coil is working normally, and gives a pre-warning. The monitoring and diagnosis mechanism principles of other oil pumps, motors, and emergency valves are similar, mainly by monitoring the amount of information that can be measured by each component (current, displacement, pressure, etc.), storing the data of the component under normal working conditions, setting a certain threshold based on experience and experimental tests, and comparing the monitoring data of the component in real time, so as to judge whether the component is working normally and whether it has deviated from the normal track, and further provide pre-diagnosis, so as to take measures in advance to avoid accidents.

[0195] like Fig.29 As shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 4 includes a processor 41 and a memory 42 coupled to the processor 41.

[0196] The memory 42 stores program instructions for implementing the intelligent control method for the speed governor of a hydropower unit according to any of the above embodiments.

[0197] The processor 41 is used to execute program instructions stored in the memory 42 to perform the layout of the chemical pump body processing equipment.

[0198] The processor 41 may also be referred to as a CPU (Central Processing Unit). The processor 41 may be an integrated circuit chip having signal processing capabilities. The processor 41 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0199] Further, Fig.30 The schematic diagram of the structure of the storage medium of an embodiment of the present application is that the storage medium 5 of the embodiment of the present application stores program instructions 51 that can implement all the above methods, wherein the program instructions 51 can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0200] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0201] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units. The above is only an implementation mode of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present invention.

[0202] The specific implementation methods of the invention are described in detail above, but they are only examples, and the invention is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the invention, therefore, the equalization, modification, improvement, etc. made without departing from the spirit and principle of the invention should be included in the scope of the invention.

Claims

1. A method for intelligent control of a speed governor of a hydropower unit, characterized in that: The intelligent control method for the speed governor of a hydropower unit comprises: The variable structure and variable parameter control technology is introduced to dynamically adjust the control parameters of the power closed loop and the opening closed loop; real-time calculation and feedback control, the speed regulator responds to the grid frequency modulation command; The sensors collect the operating data of the key components of the speed regulator in real time, including temperature, pressure, vibration and flow parameters. The collected parameters are stored and preprocessed using big data, and artificial intelligence algorithms are used to extract features and recognize patterns in the data to identify the operating status and potential faults of the speed regulator. The health status of the speed regulator is scored in real time by building a status assessment model to generate an operating status report. Based on the parameters of speed governor status monitoring, a fault tree model is constructed to perform hierarchical analysis on the speed governor to locate the specific location and cause of the fault. Combined with expert knowledge in the field of speed governors, an expert system is constructed to verify and optimize the fault diagnosis results and generate targeted fault handling suggestions. Based on the fault diagnosis results, the control parameters or operating mode of the speed governor are automatically adjusted to achieve adaptive regulation.

2. The intelligent control method for the speed governor of a hydropower unit according to claim 1 is characterized in that: The process of dynamically adjusting the control parameters of the power closed loop and the opening closed loop includes: The speed governor receives the power set value from the monitoring system, and the received power set value will be processed by standard and converted into a standardized signal inside the speed governor; the speed governor performs ramp processing on the power set value, and the power set value after ramp processing will directly act on the guide vane opening to form a pre-adjustment of the guide vane opening; In the power closed-loop mode, the speed governor's regulation goal is to make the actual output power of the generator consistent with the given power; the speed governor monitors the actual output power of the generator in real time, compares it with the given power, and calculates the power deviation; based on the power deviation, the speed governor adjusts the guide vane opening through the PID control algorithm to make the actual power gradually approach the given power; On the basis of power closed loop, the speed governor needs to perform closed-loop control on the guide vane opening to ensure that the guide vane opening matches the power demand; the speed governor monitors the actual guide vane opening in real time, compares it with the target opening, and calculates the opening deviation; based on the opening deviation, the speed governor fine-tunes the guide vane opening to ensure coordination between the opening and the power demand; During the power closed-loop and opening closed-loop control process, the speed governor dynamically adjusts the parameters of the PID controller according to the dynamic response; when the actual output power is consistent with the given power, the speed governor stops adjusting and maintains the current guide vane opening and power output; the speed governor feeds back the final power and opening status to the monitoring system.

3. The intelligent control method for the speed governor of a hydropower unit according to claim 2 is characterized in that: The process of ramping power setting includes: Based on historical data and current system status, neural networks are used to predict the changing trend of power demand, and the prediction results are used to dynamically adjust the slope of the ramp. In the process of ramp generation, adaptive smoothing filters are used to adjust the filter parameters in real time according to the dynamic response characteristics of the system. The power given signal is divided into multiple ramp segments, and different slopes are set for each segment according to the current load characteristics and dynamic response capabilities of the system. Use the real-time collected system status data to accurately predict future power demand; combine the feedforward prediction signal with PID feedback control to form a dynamic collaborative optimization mechanism. The feedforward signal is used to adjust the system in advance; PID feedback control is used to correct the error in the feedforward prediction; according to the real-time operating status of the system, the weight of the feedforward signal and the feedback signal is dynamically adjusted; Through the online performance evaluation mechanism, the system's response time, overshoot, and key stability indicators are monitored in real time; according to the evaluation results, the parameters of the ramp generator and the feedforward prediction model are dynamically adjusted; the system's response speed, stability, and energy consumption targets are incorporated into the multi-objective optimization framework; and the optimal balance point between multiple objectives is found through a genetic algorithm.

4. The intelligent control method for the speed governor of a hydropower unit according to claim 2 is characterized in that: The filtering process of power feedback includes: Adopting an adaptive filtering strategy based on a time window, the filter time constant is dynamically adjusted to maintain real-time performance under different working conditions. The filter time constant is adaptively adjusted according to the frequency characteristics of power fluctuations. The power signal is decomposed into different frequency scales through wavelet transform to filter out high-frequency clutter and burrs; the Kalman filter is used to dynamically predict the trend of power fluctuation based on the historical data and current data of power feedback, and smooth it. By combining wavelet transform and Kalman filtering, high-frequency noise is filtered out while tracking the main line of power fluctuation; By monitoring the filtered power feedback signal in real time, the degree of matching with the actual power fluctuation is evaluated, and the filtering parameters are dynamically adjusted according to the evaluation results.

5. The intelligent control method for the speed governor of a hydropower unit according to claim 1 is characterized in that: The power regulation strategy of the speed regulator includes: The speed regulator receives the power mode activation / deactivation signal, power setting mode selection signal, power regulation enable signal, and 4-20mA power setting analog signal sent by the monitoring system; the speed regulator analyzes and determines the current working mode and power regulation target; In the power closed-loop mode, the speed governor automatically performs the primary and secondary frequency regulation functions, canceling the original frequency regulation blocking strategy of the monitoring system; the speed governor dynamically adjusts the output power according to the received power given signal to ensure the optimal response and regulation performance of the unit; The speed regulator feeds back the current power mode status and 4-20mA power setting analog signal to the monitoring system for comparison and confirmation of the power setting, thus realizing the handshake mechanism between the speed regulator and the monitoring system. According to the adjustment results of the speed regulator, the monitoring system optimizes the AGC setting dead zone, adjustment dead zone, allocation strategy and frequency modulation locking logic to improve the response speed and adjustment accuracy of the AGC; at the same time, the monitoring system sends a load given signal to the speed regulator according to the AGC load distribution principle.

6. The intelligent control method for the speed governor of a hydropower unit according to claim 5 is characterized in that: The process of primary frequency regulation and power regulation includes: The speed regulator adopts the power mode, and the adjustment object of the primary frequency regulation is the active power of the unit; when the primary frequency regulation is in a steady load state and there is no external load setting, the adjustment amount of the primary frequency regulation will not be pulled back by the power regulation; when there is an external load regulation instruction, the speed regulator will give priority to responding to the power regulation request regardless of whether the primary frequency regulation is in action; The load increase and decrease caused by the primary frequency regulation is superimposed according to the principle shown in the drawing; steady-state load regulation, when the load is in a steady state, the adjustment amount of the primary frequency regulation should be independent of the power regulation; dynamic load regulation, when there is an external load regulation instruction, the speed regulator should respond to the power regulation first; The speed regulator calculates the power deviation in real time and quickly adjusts the guide vane opening according to the deviation value to compensate for the delay caused by the mechanical dead zone and the electrical dead zone. It dynamically adjusts the action amount of the primary frequency regulation according to the change of the grid frequency, monitors the action amount of the primary frequency regulation in real time, and optimizes the frequency regulation strategy according to historical data. Monitor the action amount of primary frequency regulation in real time and compare it with the theoretical calculated amount; automatically adjust the frequency regulation strategy according to the power grid assessment standards.

7. The intelligent control method for the speed governor of a hydropower unit according to claim 5 is characterized in that: The process of switching between the speed regulator power mode and the opening mode includes: The speed regulator is regulated in power mode under normal operation, and the speed regulator directly responds to the power command; the opening closed loop is used as a backup plan for the power mode, and maintains the basic load adjustment capability when the power mode is abnormal; When the power closed-loop regulation system detects a fault or anomaly, it automatically identifies and records the fault type and time; after the fault is identified, the speed regulator automatically switches to the opening mode; in some cases, the operator chooses to manually switch to the opening mode according to the actual situation; When the fault that causes the automatic exit of the power closed loop is repaired, the fault recovery status is automatically detected; no matter whether the fault recovery is completed automatically or manually intervened, it will not automatically switch from the opening mode to the power mode; after the operator's confirmation, the speed regulator switches from the opening mode to the power mode; after the mode switching is completed, the operating status of the speed regulator is monitored in real time.

8. The intelligent control method for the speed governor of a hydropower unit according to claim 1 is characterized in that: The variable parameter control process of the speed regulator includes the following steps: Get the current speed regulator parameter value and determine whether the current parameter value is within the preset reasonable range; if it is not within the range, it needs to be adjusted; If the parameter value is within the range, it is further determined whether it meets the set judgment standard; if not, the parameter needs to be adjusted; according to the judgment result, the speed regulator parameter is adjusted to change in the direction of meeting the standard or target value; Repeat the above steps until the speed regulator parameters are stable within a reasonable range and meet the judgment criteria.

9. The intelligent control method for the speed governor of a hydropower unit according to claim 1 is characterized in that: The process of identifying the operating status and potential failures of the speed regulator, The following steps are involved: The sensors installed in various parts of the speed control system will monitor various physical signals in real time and convert the physical signals into electrical signals, which will be transmitted to the industrial computer through the speed control cabinet. After receiving the electrical signals, the industrial computer will perform preliminary data processing and analyze and extract characteristic parameters reflecting the operating status of the speed control system. The extracted characteristic parameters are monitored in real time and compared with the preset normal operating range. If some parameters exceed the normal range, an alarm is triggered to indicate that there may be a fault. When an abnormality is detected, fault recording is automatically performed, that is, all relevant data before and after the fault occurs are recorded. The data is stored in the industrial computer and uploaded to the background comprehensive diagnosis system regularly. After receiving these data, the background comprehensive diagnosis system analyzes them and generates status inspection logs and status development trend reports. Based on the analysis results, it provides auxiliary decision-making suggestions, such as whether status maintenance is needed and when to perform the maintenance. It is displayed through the human-machine interface and uploaded to the background comprehensive diagnosis system for reference by operation and maintenance personnel. All generated status data, analysis results and diagnostic reports are shared through the network, allowing local or remote user terminals to query and download data through the browser.

10. An intelligent control system for a speed governor of a hydropower unit, applied to the intelligent control method for a speed governor of a hydropower unit as claimed in any one of claims 1 to 9, characterized in that: The hydropower unit speed governor intelligent control system comprises: Dynamic adjustment module, used to dynamically adjust the control parameters of power closed loop and opening closed loop by introducing variable structure and variable parameter control technology; real-time calculation and feedback control, the speed regulator responds to the grid frequency regulation command; The status assessment module is used to collect the operating data of the key components of the speed regulator in real time through sensors, store and preprocess the collected parameters using big data, and perform feature extraction and pattern recognition on the data in combination with artificial intelligence algorithms to identify the operating status and potential faults of the speed regulator; by building a status assessment model, the health status of the speed regulator is scored in real time and an operating status report is generated; The fault analysis module is used to build a fault tree model based on the parameters of the speed regulator status monitoring, conduct hierarchical analysis of the speed regulator, and locate the specific location and cause of the fault; combine the expert knowledge in the field of speed regulators to build an expert system to verify and optimize the fault diagnosis results and generate targeted fault handling suggestions; according to the fault diagnosis results, the control parameters or operating mode of the speed regulator are automatically adjusted to achieve adaptive regulation.

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