Intelligent control method for disc turning equipment of a hydroelectric generator set

Abstract

The application relates to the technical field of intelligent control, and provides an intelligent control method for disc car equipment of a hydroelectric generating set. The method comprises the following steps: collecting and measuring a target generating set, performing digital-to-analog conversion, and obtaining a to-be-conditioned signal set. The to-be-conditioned signal is amplified and conditioned, and a conditioned signal set is output. Control processing is performed according to the conditioned signal set, preliminary control parameters are obtained, the preliminary control parameters are optimized, optimal control parameters are obtained, fitting simulation is performed, control fitting parameters are obtained, and judgment is performed to generate disc car control parameters; and equipment control is performed based on the disc car control parameters. The application solves the technical problem that, when facing a complex and changeable operation environment and a generating set state, an existing control method is difficult to accurately and quickly adjust control parameters, leading to low operation efficiency and great safety hazards of the generating set, realizes intelligent and automatic control of the disc car equipment of the hydroelectric generating set, and improves the technical effects of maintenance efficiency and accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automation, specifically to the technical field of intelligent control, and particularly to an intelligent control method for water turbine generator set turning gear equipment. BACKGROUND

[0002] With the rapid development of the energy industry and the widespread application of clean energy technology, water turbine generator sets, as the core equipment of hydroelectric power generation, their operating efficiency and safety are crucial to ensuring power supply and energy saving and emission reduction. However, in the actual operation process, water turbine generator sets face complex and variable operating environments and unit states, which puts higher requirements on the control and management of turning gear equipment. The existing control method of water turbine generator set turning gear equipment cannot respond and handle in a timely manner when facing complex operating environments or unit faults, resulting in low operating efficiency of the unit and even safety hazards, which brings risks to power production. SUMMARY

[0003] The present application provides an intelligent control method for water turbine generator set turning gear equipment, which solves the technical problem that the existing control method cannot accurately and quickly adjust the control parameters when facing complex and variable operating environments and unit states, resulting in low operating efficiency of the unit and large safety hazards.

[0004] In view of the above problems, the present application provides an intelligent control method for water turbine generator set turning gear equipment, which comprises:

[0005] The data sensor is used to collect and measure the swing data and rotation angle data of the target unit, and the data collector is used for digital-to-analog conversion to obtain a set of signals to be conditioned;

[0006] The set of signals to be conditioned is input into a single-chip differential amplifier for amplification and key signal conditioning, and a set of conditioned signals is output;

[0007] The turning gear data control processing is performed according to the set of conditioned signals to obtain preliminary control parameters of the turning gear, and the parameter optimization is performed according to the preliminary control parameters of the turning gear to obtain an optimal position set as optimal control parameters of the turning gear;

[0008] The turning gear data fitting simulation is performed according to the optimal control parameters of the turning gear to obtain turning gear control fitting parameters, and the actuality judgment is performed to generate the turning gear control parameters;

[0009] The turning gear equipment control of the target unit is performed based on the turning gear control parameters.

[0010] The method includes collecting swing data and rotation angle data of the target unit at the upper guide bearing, lower guide bearing and water guide bearing positions.

[0011] The method comprises: calling a measuring point, and selecting a layout position of the data sensor based on the measuring point;

[0012] The total clearance between the same main shafts of the data sensor is less than or equal to the linear segment of the data sensor, wherein the total clearance between the same main shafts comprises an average clearance between the same main shafts and a runout clearance.

[0013] The method comprises: the conditioning signal set comprises an amplified differential signal and a conditioned key signal.

[0014] The method comprises: a configuration requirement design, comprising a turning parameter setting function requirement, a turning measurement function requirement and a turning calculation function requirement;

[0015] The turning parameter setting comprises target unit parameter setting, data sensor setting and turning model setting.

[0016] The turning measurement comprises manual mode measurement and automatic mode measurement.

[0017] The turning calculation comprises turning data calculation, turning axis output, turning adjustment suggestion, turning adjustment simulation, turning table output and turning data saving.

[0018] An optimal position set is obtained, comprising:

[0019] A decision particle set is generated according to the turning preliminary control parameter, and a first decision particle is obtained based on the decision particle set.

[0020] A first speed is iteratively updated according to the learning factor and the first decision particle, the flight distance and direction are controlled according to the first speed, and a first position is generated, wherein the first position is iteratively updated t times, and t is an integer greater than 1.

[0021] A first optimal position is generated according to a decision particle optimal position calculation function, and the optimal position set is obtained.

[0022] The decision particle optimal position calculation function is as follows:

[0023] ;

[0024] In the formula, denotes a position vector of the i th decision particle at t th moment, denotes a position of the i th decision particle at t th moment, denotes a position of the i th decision particle at t-1 th moment, denotes an adaptive value of the i th decision particle at t th moment, denotes an adaptive value of the i th decision particle at t-1 th moment, and the position change of the decision particle set is related to the adaptive value.

[0025] The disc rotation control fitting parameter is obtained, including:

[0026] The disc rotation control fitting parameter is obtained, including:

[0027] The disc rotation control fitting parameter is obtained, including:

[0028] The disc rotation control fitting parameter is obtained, including:

[0029] The ideal control parameter is called based on the water turbine parameter, and the allowable control threshold is obtained based on the ideal control parameter.

[0030] The disc rotation control fitting parameter is obtained, including:

[0031] The disc rotation control fitting parameter is obtained, including:

[0032] The disc rotation control fitting parameter is obtained, including:

[0033] One or more technical solutions provided in the application have at least the following technical effects or advantages:

[0034] The above-mentioned intelligent control method of the disc rotation equipment of the hydroelectric generating set, by using the data sensor, the swing and rotation angle data of each key part of the unit are measured in real time. These data are converted into a group of signals to be conditioned after the digital-to-analog conversion of the data collector. Then, the signals to be conditioned are input into the signal single-chip differential amplifier for signal amplification and key signal conditioning to obtain a clearer and more accurate signal set. Then, based on the conditioned signals, the disc rotation data is controlled and processed to preliminarily obtain the disc rotation control parameter. In order to further improve the accuracy of the control, the preliminary parameters are optimized to find the optimal disc rotation control parameter. After obtaining the optimal control parameter, the disc rotation data is simulated, the actual operation of the unit is simulated, and a more actual disc rotation control fitting parameter is obtained. After a series of verification and judgment, the disc rotation control parameter for controlling the disc rotation equipment is finally generated. Then, based on the disc rotation control parameter, the intelligent and automatic control of the disc rotation equipment of the hydroelectric generating set is realized, thereby improving the operation efficiency, safety and stability of the unit, and also improving the efficiency and accuracy of subsequent maintenance.

[0035] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating an intelligent control method for a turning gear of a hydro-generator unit in one embodiment.

[0038] Figure 2 This is a flowchart illustrating the process of obtaining the optimal position set in an intelligent control method for a hydro-generator unit turning gear in one embodiment. Detailed Implementation

[0039] This application provides an intelligent control method for the turning gear of a hydro-generator unit, which solves the technical problem that existing control methods are unable to accurately and quickly adjust control parameters when facing complex and ever-changing operating environments and unit states, resulting in low unit operating efficiency and significant safety hazards.

[0040] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0041] It should be noted that the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product, or device.

[0042] Examples, such as Figure 1 As shown, this application provides an intelligent control method for the turning gear of a hydro-generator unit, the method comprising:

[0043] The data sensor is used to collect and measure the swing data and rotation angle data of the target unit, and the data collector is used for digital-analog conversion to obtain a set of signals to be conditioned.

[0044] During operation, hydroelectric generating units may encounter various complex operating conditions and faults, such as overload and excessive vibration. Traditional control methods often fail to timely detect and handle these problems, thereby posing a risk to the safe operation of the unit. An intelligent control method for a hydroelectric generating unit turning gear device can monitor the operating state of the unit in real time, and once an abnormal condition is detected, appropriate control measures can be taken immediately to ensure the safe operation of the unit and improve the efficiency and accuracy of subsequent maintenance.

[0045] In the embodiments of the present application, when the hydroelectric generating unit is precisely controlled, the system terminal first uses a data sensor to collect important data about the unit in real time, including the swing and rotation angle of each key part of the unit. These data are crucial for understanding the operating state of the unit. Once the sensor collects these data, the system terminal processes them through a data collector. The function of this device is to convert the analog signals collected by the sensor into digital signals, i.e., digital-analog conversion. The converted digital signals are more convenient for computer processing and analysis. After digital-analog conversion, the system terminal obtains a set of signals to be conditioned. These signal sets contain key information about the operating state of the unit and are the basis for subsequent intelligent control. In summary, this process uses data sensors and data collectors to realize real-time collection and digital processing of key operating data of hydroelectric generating units, providing necessary data support for subsequent intelligent control.

[0046] Further, the present application provides a collection site of swing data and rotation angle data of a target unit, and the method further comprises:

[0047] The collection site of swing data and rotation angle data of the target unit includes the upper guide bearing, the lower guide bearing, and the water guide bearing site.

[0048] Preferably, during the operation of the target unit, in order to accurately grasp its running state and performance parameters, the system terminal collects swing data and rotation angle data of multiple key positions. These data are collected from key positions such as the upper guide bearing, the lower guide bearing, and the water guide bearing. The upper guide bearing is located at the upper part of the unit and mainly bears the weight of the upper part of the unit and the radial force during rotation; the lower guide bearing is located at the lower part of the unit and also bears the weight and radial force, but the position is different; and the water guide bearing is directly related to the water flow and is the key interface between the unit and the water turbine rotor. Collecting swing data and rotation angle data from these three positions can comprehensively understand the subtle changes of the unit during rotation. Swing data reflects the deviation of the unit's centerline and is an important basis for evaluating the stability and balance of the unit; and rotation angle data is directly related to the rotation speed and accuracy of the unit, which is crucial for ensuring the normal operation of the unit and the power generation efficiency.

[0049] Further, the application provides an arrangement of data sensors, and the method further comprises:

[0050] calling a measurement point to select the arrangement position of the data sensor based on the measurement point;

[0051] The total clearance between the same main shaft and the data sensor is less than or equal to the linear segment of the data sensor, wherein the total clearance between the same main shaft includes the average clearance between the same main shaft and the swing clearance.

[0052] Preferably, when intelligent control of the water turbine generator unit turning gear equipment is performed, the system terminal first selects appropriate measurement points to determine the arrangement position of the data sensor. These measurement points are selected according to the characteristics and control requirements of the unit to ensure that key parameters can be accurately monitored. When arranging the data sensor, the system terminal focuses on the clearance between the sensor and the main shaft. Specifically, when arranging, the total clearance between the same main shaft needs to be less than or equal to the linear segment of the data sensor, that is, the sum of the average clearance between the same main shaft and the swing clearance needs to be less than or equal to the linear segment of the data sensor. This is because the data sensor has the highest measurement accuracy and stability within the linear segment. If the clearance is too large, the measurement data will be inaccurate, thereby affecting the performance of the entire control system. In summary, the selection of measurement points and the determination of the arrangement position of data sensors are a very key step in the intelligent control of the water turbine generator unit turning gear equipment. By selecting measurement points and reasonably arranging data sensors, the system terminal can accurately and reliably monitor the running state of the unit, providing strong data support for subsequent intelligent control.

[0053] Further, the application provides a configuration requirement design, and the method further comprises:

[0054] The configuration requirement design includes discarding parameter setting function requirements, discarding measurement function requirements, and discarding calculation function requirements;

[0055] The disc wheel parameter setting includes target unit parameter setting, data sensor setting and disc wheel model setting.

[0056] The disc wheel measurement includes manual mode measurement and automatic mode measurement.

[0057] The disc wheel calculation includes disc wheel data calculation, disc wheel axis output, disc wheel adjustment suggestion, disc wheel adjustment simulation, disc wheel table output and disc wheel data saving.

[0058] Preferably, in the intelligent control of the disc wheel equipment of the hydroelectric generating unit, the configuration requirement design is a crucial link, which ensures that the system terminal can fully and accurately meet the actual operation requirements. These configuration requirements mainly include disc wheel parameter setting, disc wheel measurement and disc wheel calculation. First, disc wheel parameter setting is the basis for the intelligent control of the system terminal, which covers target unit parameter setting, data sensor setting and disc wheel model setting. By setting the parameters of the target unit, the system terminal can understand the characteristics of the unit; the setting of the data sensor determines which key data the system terminal can obtain; the disc wheel model setting is based on the unit parameters and sensor data to establish a mathematical model for analysis and calculation. Second, disc wheel measurement is a key step for the system terminal to obtain real-time data. It provides manual mode measurement and automatic mode measurement to adapt to different operation requirements and scenarios. Manual mode measurement allows operators to select measurement points for accurate measurement according to actual needs; while automatic mode measurement automatically collects and records data based on pre-set measurement points and sensors. Finally, disc wheel calculation is the core step of the system terminal to process and analyze the measurement data. It includes disc wheel data calculation, disc wheel axis output, disc wheel adjustment suggestion, disc wheel adjustment simulation, disc wheel table output and disc wheel data saving. Through the calculation and analysis of the measurement data, the running state of the unit, the axis offset and other information can be obtained; and according to these information, the corresponding adjustment suggestions are given; at the same time, through the simulation function, the effect of adjustment is simulated to help the operator more intuitively understand the influence of adjustment; the system terminal also saves the calculation results and data for subsequent analysis and reference. Through the above configuration requirement design, the system terminal can fully and accurately meet the requirements of disc wheel control. From disc wheel parameter setting, disc wheel measurement to disc wheel calculation, each link is closely linked, which constitutes the core function of the system terminal.

[0059] The to-be-conditioned signal set input signal single-chip differential amplifier is amplified and keying signal conditioning is performed, and a conditioned signal set is output;

[0060] In one embodiment, when performing intelligent control of the hydroelectric generator set turning gear equipment, for the set of signals to be conditioned obtained from the data collector, the system terminal will perform a series of processing to ensure its accuracy and usability. One of the key steps is to input the set of signals to be conditioned into the signal monolithic differential amplifier for amplification. The signal monolithic differential amplifier is an electronic component specifically designed to amplify differential signals. Differential signals refer to the difference between two signals of opposite phase, which can eliminate common-mode noise and interference, and improve the signal-to-noise ratio of the signal. Inside the differential amplifier, the voltage difference between the positive input and the inverting input is amplified. This amplification process depends on the design characteristics of the differential amplifier and the internal amplification factor setting. If the amplitudes of the two input signals are the same, i.e. there is no difference, the output of the differential amplifier is zero or close to zero. But if the amplitudes of the two input signals are different, the differential amplifier will output an amplified differential signal, making it easier to identify and process later. In addition to amplifying the signal, the key signal is also conditioned. The key signal is a synchronization signal that is used to determine the angular position of the rotating equipment. In the hydroelectric generator set, the key signal corresponds to the rotation angle of the rotor. The conditioning process includes signal amplification, filtering, synchronization, etc. to ensure that it is synchronized and accurate with the output signal of the differential amplifier, providing a reliable reference for subsequent turning gear data control processing. After amplification by the signal monolithic differential amplifier and conditioning of the key signal, the system terminal combines the output signal of the differential amplifier and the conditioned key signal to form a conditioned signal set. In summary, inputting the signal to be conditioned into the signal monolithic differential amplifier for amplification and conditioning the key signal is an important signal processing step in intelligent control of the hydroelectric generator set turning gear equipment. It ensures the accuracy and usability of the signal, providing a reliable input for subsequent control processing.

[0061] Further, the present application provides a conditioned signal set, and the method further comprises:

[0062] The conditioned signal set comprises the amplified differential signal and the conditioned key signal.

[0063] Preferably, the conditioned signal set comprises the amplified differential signal and the conditioned key signal. The amplified differential signal is the result of amplifying the originally weak differential signal. The differential signal is obtained by comparing the measurement values at two similar positions or under similar conditions, which can effectively suppress common-mode noise and improve the signal-to-noise ratio of the signal. The amplification step is to further enhance the strength of the signal, making it easier to be recognized and processed by the subsequent processing system. The conditioned key signal is the result of a series of conditioning operations on the original key signal. The key signal is used to identify the speed and position information of the rotating equipment.

[0064] According to the set of conditioned signals, a turning gear data control process is performed to obtain preliminary turning gear control parameters, and according to the preliminary turning gear control parameters, parameter optimization is performed to obtain an optimal position set as optimal turning gear control parameters.

[0065] In one embodiment, when performing intelligent control of the water turbine generator set turning gear equipment, after the system terminal obtains the set of conditioned signals, the system terminal performs a turning gear data control process on the signals. This process involves analysis and calculation of the operating state of the unit. First, the system terminal analyzes the set of conditioned signals. This includes identifying key features in the signals, extracting useful information, etc. For example, analyze the swing, rotation angle, etc. of the unit to understand the operating state of the unit. Then, based on the results of the signal analysis, the operating state of the unit is judged. This process will combine historical data of the unit, operating experience and expert knowledge base to identify possible problems or abnormal conditions of the unit. For example, detect whether the unit has excessive swing, unstable rotation angle, etc. After judging the operating state of the unit, the system terminal calculates preliminary turning gear control parameters based on the state information. These parameters include the adjustment amount, adjustment speed, adjustment direction, etc. of the unit, aiming to restore the unit to normal operating state or optimize its operating performance. After obtaining the preliminary turning gear control parameters, the system terminal performs parameter optimization on the preliminary turning gear control parameters according to the learning factor and the optimal position calculation function of the decision particle to obtain an optimal position set. This set contains optimized control parameter values. These parameter values will be used as optimal turning gear control parameters because they can meet various constraint conditions while optimizing the operation of the unit. In summary, the process of performing a turning gear data control process based on the set of conditioned signals and obtaining preliminary control parameters, and then optimizing these parameters to obtain optimal turning gear control parameters, is a key step in achieving intelligent control of the water turbine generator set turning gear equipment. This process ensures that the unit can operate in an optimal state, improving the operating efficiency and safety of the unit.

[0066] Further, as shown in Figure 2 The method further includes:

[0067] According to the preliminary turning gear control parameters, a set of decision particles is generated, and a first decision particle is obtained based on the set of decision particles.

[0068] According to the learning factor, the first decision particle is iteratively updated at a first speed, and the first speed controls the flight distance and direction to generate a first position, wherein the first position is iteratively updated t times, and t is an integer greater than 1.

[0069] Preferably, the system terminal initializes a set of decision particles according to the definition domain and possible value range of the preliminary control parameters of the disc turning. Each decision particle represents a set of possible control parameters, which together constitute a set of candidate solutions in the search space. The number of decision particles is determined according to the complexity of the actual problem and the computing resources. The more the number of particles, the stronger the search ability, but the corresponding increase in the cost of computing. Subsequently, the system terminal randomly selects an initial decision particle from the set of decision particles as the first decision particle. Before starting the iteration, the system terminal randomly initializes an initial speed and position for the first decision particle. The speed represents the moving direction and speed size of the particle in the search space, while the position represents the current position of the particle in the search space. For the first decision particle, the fitness value is calculated according to the position, i.e. the value of the control parameters. The fitness value reflects the good or bad of the control parameters, which is evaluated by the position change. Then, in each iteration, the speed of the first decision particle is updated according to the learning factor. The learning factor includes the cognitive learning factor and the social learning factor, which are used to adjust the degree of learning of the particle to its own historical optimal position and the global optimal position, respectively. When updating the speed, the system terminal considers the current speed of the particle, the difference between the individual optimal solution and the current position, and the difference between the global optimal solution and the current position. These differences are multiplied by the corresponding learning factor, and then added to a certain inertia weight, to finally obtain the new speed. The inertia weight is used to maintain the inertia of the particle motion, which is adaptively adjusted by the system terminal according to the actual situation and historical experience. Because a larger inertia weight is conducive to exploration, but may lead to slower convergence speed; while a smaller inertia weight is conducive to development, but may lead to the algorithm falling into a local optimal solution. Then, the system terminal uses the updated speed to control the flight distance and direction of the first decision particle. The new speed vector is added to the current position vector to obtain a new position. In this way, the first decision particle moves to a new position according to the new speed. Repeat the above process t times, where t is the preset number of iteration updates, which is an integer greater than 1. In each iteration, the speed of the first decision particle is updated according to the learning factor, and the flight distance and direction are controlled according to the new position, i.e. the first position, generated thereby. This position represents a set of optimized control parameters.

[0070] The first optimal position is generated according to the decision particle optimal position calculation function, and the optimal position set is obtained.

[0071] Preferably, after the first decision particle moves to a new position (the first position) based on its updated velocity and direction, the system terminal uses the decision particle optimal position calculation function to evaluate the performance of this new position. This function calculates the merits of the new position based on the particle's current fitness value. If the performance of the first position is better than the optimal position previously found by the first decision particle, then the first position becomes the new first optimal position. This process occurs in each iteration because the particle continuously updates its velocity and position and searches for better solutions in the search space. As the number of iterations increases, the system terminal records all the optimal positions found by the decision particles, forming an optimal position set. This set contains all the excellent candidate solutions found during the search process, all obtained based on different iterations and particle updates.

[0072] Furthermore, this application provides a function for calculating the optimal position of the decision particle, and the method further includes:

[0073] The function for calculating the optimal position of the decision particle is as follows:

[0074] ;

[0075] Optionally, the optimal position calculation function for decision particles is a crucial step in determining the optimal position of each decision particle during the search process. Specifically, the optimal position calculation function for decision particles is as follows:

[0076] ;

[0077] In the formula, The position vector representing the i-th decision particle at time t represents the particle's current position in the search space, i.e., a set of possible control parameter values. It represents the position of the i-th decision particle at time t. It records the optimal position found by the particle during the search process, which is the best set of control parameter values ​​found by the particle so far. This represents the position of the i-th decision particle at time t-1. During the iteration process, the system terminal compares the current position with the previous individual's optimal position to update the position. . The fitness value represents the fitness value of the i-th decision particle at time t. The fitness value is obtained by evaluating the degree of position change after each iteration. The greater the position change, the smaller the fitness value, reflecting the performance of the current position. The fitness value of the i-th decision particle at time t-1 is used to compare with the fitness value at its current position to determine whether the individual's optimal position needs to be updated. In each iteration, the system terminal evaluates the current position of each decision particle. fitness value and compare it with the previous individual optimal position of the fitness value If the fitness value of the current position is better, i.e., the fitness value is smaller, the system terminal sets the current position as the new individual optimal position and updates the corresponding fitness value.

[0078] According to the optimal disc turning control parameters, disc turning data fitting simulation is performed to obtain disc turning control fitting parameters, and a real compliance judgment is made to generate disc turning control parameters;

[0079] In one embodiment, when performing intelligent control of the disc turning equipment of the hydroelectric generator unit, once the optimal disc turning control parameters are determined through particle swarm optimization, the system terminal performs disc turning data fitting simulation. This process is to verify and optimize the performance of these parameters in actual operation and to ensure that they meet the actual engineering requirements. Specifically, the system terminal configures simulation fitting parameters according to the optimal disc turning control parameters, and performs simulation and simulation through the configured parameters to obtain curve fitting results. Subsequently, by comparing with the preset local threshold, it is judged whether the current optimal disc turning control parameters are local optimal, and if the judgment result is not to meet the local threshold, the system terminal generates disc turning control fitting parameters. After obtaining the disc turning control fitting parameters, the next step is to make a real compliance judgment. This step is to ensure that the simulation results can truly reflect the actual running condition of the disc turning control system. By comparing the simulation data with the allowable control threshold, the effectiveness and accuracy of the optimal disc turning control parameters can be evaluated. If it is found that there is a large difference between the simulation results and the allowable control threshold, the control parameters need to be adjusted until the simulation results coincide with the allowable control threshold. Otherwise, the system terminal takes the disc turning control fitting parameters as the disc turning control parameters for actual disc turning control. These parameters can ensure that the disc turning operation is carried out in an efficient, stable and safe state, improve the performance and reliability of the control, and the efficiency and accuracy of the subsequent maintenance.

[0080] Further, the present application provides a method for obtaining disc turning control fitting parameters, which further comprises:

[0081] configuring simulation fitting parameters according to the optimal disc turning control parameters, and performing simulation to obtain curve fitting results;

[0082] judging whether the curve fitting results meet the local threshold, and if not, obtaining the disc turning control fitting parameters.

[0083] Preferably, when the intelligent control of the hydroelectric generator set's turning gear equipment is being performed, after the optimal turning gear control parameters are determined, the system terminal configures these parameters into the simulation environment for fitting simulation. The purpose of this simulation is to simulate the actual operation of the turning gear equipment and evaluate the actual effect of these parameters by obtaining the curve fitting results. First, the system terminal configures the simulation fitting parameters according to the optimal turning gear control parameters, i.e., sets the relevant parameters in the simulation component in order to simulate the actual operation of the turning gear equipment. These fitting parameters are related to key variables in the turning process, including speed, torque, temperature, etc. Then, the simulation component is run to simulate the start, operation, and stop of the turning gear equipment, etc. During the simulation process, the system terminal records and generates a series of data curves related to the turning operation, which describe the state changes of the turning gear equipment at different time points. After that, the obtained curve fitting results are evaluated. The evaluation standard is whether the curve meets the preset local threshold. The local threshold is set according to the actual needs and performance requirements of the turning gear equipment, and is used to judge whether the simulation result meets the expectation. If the curve fitting result does not meet the local threshold, it means that the current turning gear control parameters are optimal. In this case, the system terminal obtains the turning gear control fitting parameters, i.e., the data related to the turning operation generated during the simulation process, which will be used to generate the turning gear control parameters later.

[0084] Further, the application provides a method for generating turning gear control parameters, which further comprises:

[0085] calling ideal control parameters based on the parameters of the hydraulic turbine, obtaining an allowable control threshold based on the ideal control parameters;

[0086] generating the turning gear control parameters according to whether the turning gear control fitting parameters meet the allowable control threshold.

[0087] Preferably, the system terminal calls ideal control parameters based on the specific parameters of the hydraulic turbine. These ideal control parameters are derived based on factors such as the design characteristics, working environment, and performance requirements of the hydraulic turbine, aiming to achieve the best operating state of the turning gear system. Once the ideal control parameters are determined, the system terminal will set the allowable control threshold based on these parameters. The allowable control threshold is a range that defines the acceptable fluctuation range of the turning gear control parameters in actual operation. This range takes into account factors such as the stability, safety, and efficiency of the turning gear equipment, ensuring that the turning gear equipment can maintain an acceptable performance level in actual operation. Then, the system terminal compares the turning gear control fitting parameters with the set allowable control threshold. If the turning gear control fitting parameters fall within the allowable control threshold range, it means that the current control parameter settings are reasonable and can meet the operating requirements of the turning gear equipment. In this case, the system terminal takes these fitting parameters as the final turning gear control parameters for actual control.

[0088] Further, the application provides that the allowable control threshold is not met, and the method further comprises:

[0089] If the allowable control threshold is not met, an automatic adjustment scheme is generated, including water guide inclination value and machine shaft processing direction;

[0090] The disc running control fitting parameters are optimized according to the automatic adjustment scheme until the allowable control threshold is met.

[0091] Optionally, when the disc running control fitting parameters do not meet the set allowable control threshold, it means that the current control parameter settings cannot make the disc running equipment meet the expected performance requirements. In this case, the system terminal generates an automatic adjustment scheme to optimize the disc running control parameters. The automatic adjustment scheme includes adjustment suggestions for the water guide inclination value and the machine shaft processing direction. The water guide inclination value is one of the key parameters for controlling the direction and efficiency of the water flow of the water turbine, and the machine shaft processing direction involves adjustment of the mechanical part of the disc running equipment. These adjustment suggestions are based on in-depth analysis of the current equipment state and preset optimization algorithms. According to the automatic adjustment scheme, the system terminal optimizes the disc running control fitting parameters. This includes gradually adjusting the water guide inclination value and the machine shaft processing direction, and re-simulating fitting to obtain new disc running control fitting parameters. Then, the system terminal compares these new fitting parameters with the allowable control threshold to determine whether the requirements are met. If the new fitting parameters still do not meet the allowable control threshold, the system terminal will continue to adjust automatically and repeat the above process until a suitable set of control parameters is found, enabling the disc running equipment to meet the expected performance requirements. This process is an iterative optimization process, aiming to find the optimal disc running control parameter settings through continuous trial and adjustment.

[0092] The disc running equipment control of the target unit is performed based on the disc running control parameters.

[0093] In one embodiment, when the disc running control parameters are optimized and meet the allowable control threshold, these parameters will be applied to the actual target unit disc running equipment control. These disc running control parameters involve the start-up, running speed, acceleration, deceleration, stop time, and other aspects of the equipment, ensuring that the equipment can meet the predetermined performance indicators such as stability, efficiency, safety, etc. when running. By applying these optimized control parameters, it is expected that the target unit disc running equipment will perform better in actual operation, reducing unnecessary wear and tear, prolonging the service life of the equipment, and improving subsequent maintenance efficiency and accuracy.

[0094] In summary, the embodiments of the application have at least the following technical effects:

[0095] The embodiment of the application collects the swing and rotation angle data of the upper guide bearing, the lower guide bearing and the water guide bearing of the target unit through the data sensor, and obtains the preliminary control parameters of the turning gear data control processing after digital-to-analog conversion and signal conditioning. Then, the optimal position set, i.e., the optimal turning gear control parameters, is obtained through parameter optimization. Subsequently, the turning gear data fitting simulation is performed based on the optimal control parameters, and the actual judgment is performed according to the simulation results and the allowed control threshold to generate the turning gear control parameters for the actual turning gear equipment control. In the process of obtaining the optimal position set, the speed and position of the decision particle are iteratively updated through the particle swarm optimization algorithm to find the optimal solution. At the same time, the decision particle optimal position calculation function is defined to evaluate the performance of the decision particle position. In the process of obtaining the turning gear control fitting parameters, the simulation is performed by configuring the simulation fitting parameters, and the judgment is performed according to whether the curve fitting result meets the local threshold. If not, the turning gear control fitting parameters are optimized according to the automatic adjustment scheme until the allowed control threshold is met. The finally generated turning gear control parameters will be used for the turning gear equipment control of the target unit to ensure the stability and efficiency of the turning process. These technical effects collectively solve the technical problems that the existing control method is difficult to accurately and quickly adjust the control parameters in the face of complex and variable operating environment and unit state, resulting in low unit operation efficiency and large safety hidden danger, and realize the intelligent and automatic control of the water turbine generator unit turning gear equipment, and improve the maintenance efficiency and accuracy.

[0096] It should be noted that the above sequence of the embodiments of the application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above describes a specific embodiment of the present application. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0097] The above only describes the preferred embodiments of the application and does not limit the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall be included in the protection scope of the application.

[0098] The present application and the drawings are only exemplary descriptions of the application, and are considered to cover any and all modifications, changes, combinations or equivalents within the scope of the application. Obviously, those skilled in the art can make various modifications and changes to the application without departing from the scope of the application. Thus, if these modifications and changes of the application belong to the scope of the application and its equivalents, the application intends to include these modifications and changes.

Claims

1. An intelligent control method for a hydroelectric generator unit turning gear device, characterized in that, The method comprises: Collecting swing data and rotation angle data of the target unit by using a data sensor, and performing digital-to-analog conversion by a data collector to obtain a set of signals to be conditioned; Inputting the set of signals to be conditioned into a signal single-chip differential amplifier for amplification and keying signal conditioning to output a set of conditioned signals; Performing data control processing on the set of conditioned signals to obtain preliminary control parameters for turning gear, and performing parameter optimization based on the preliminary control parameters to obtain an optimal position set as optimal control parameters for turning gear; Performing data fitting simulation based on the optimal control parameters for turning gear to obtain turning gear control fitting parameters, and performing actuality conformity judgment to generate turning gear control parameters; Controlling the turning gear equipment of the target unit based on the turning gear control parameters; The collection positions of the swing data and the rotation angle data of the target unit include upper guide bearing, lower guide bearing, and water guide bearing positions; Calling a measurement point, and selecting the arrangement position of the data sensor based on the measurement point; The total clearance between the data sensor and the main shaft is less than or equal to the linear section of the data sensor, wherein the total clearance between the data sensor and the main shaft includes an average clearance between the data sensor and the main shaft and a swing clearance; The set of conditioned signals includes amplified differential signals and conditioned keying signals; Configuring requirement design, including turning gear parameter setting functional requirement, turning gear measurement functional requirement, and turning gear calculation functional requirement; The turning gear parameter setting includes target unit parameter setting, data sensor setting, and turning gear model setting; The turning gear measurement includes manual mode measurement and automatic mode measurement; The turning gear calculation includes turning gear data calculation, turning gear axis output, turning gear adjustment suggestion, turning gear adjustment simulation, turning gear table output, and turning gear data saving; Obtaining an optimal position set comprises: Generating a decision particle set based on the preliminary control parameters for turning gear, and obtaining a first decision particle based on the decision particle set; Iteratively updating a first speed of the first decision particle according to a learning factor, controlling the flight distance and direction according to the first speed, and generating a first position, wherein the first position is iteratively updated t times, and t is an integer greater than 1; Generating a first optimal position based on a decision particle optimal position calculation function to obtain the optimal position set.

2. The intelligent control method of a hydroelectric generator unit disc turning equipment according to claim 1, characterized in that, The decision particle optimal position calculation function is as follows: ； wherein, a position vector of the i-th decision particle at time t, a position of the i-th decision particle at time t, a position of the i-th decision particle at time t-1, a fitness value of the i-th decision particle at time t, a fitness value of the i-th decision particle at time t-1, the position change of the set of decision particles is related to the fitness value.

3. The intelligent control method of a hydroelectric generator unit disc turning equipment according to claim 1, characterized in that, Obtaining turning gear control fitting parameters comprises: Configuring simulation fitting parameters based on the optimal control parameters for turning gear, and obtaining a curve fitting result through simulation; Judging whether the curve fitting result meets a local threshold value, and if not, obtaining the turning gear control fitting parameters.

4. The intelligent control method of a hydroelectric generator unit disc turning equipment according to claim 1, characterized in that, Generating turning gear control parameters comprises: Calling ideal control parameters based on water turbine parameters, and obtaining an allowable control threshold value based on the ideal control parameters; If the turning gear control fitting parameters meet the allowable control threshold value, the turning gear control parameters are generated.

5. The intelligent control method of a hydroelectric generator unit disc turning equipment according to claim 4, characterized in that, If the turning gear control fitting parameters do not meet the allowable control threshold value, an automatic adjustment scheme is generated, including a water guide inclination value and a turbine shaft processing direction; Optimizing the turning gear control fitting parameters based on the automatic adjustment scheme until the allowable control threshold value is met.

Images