Wide-width water head hydroelectric generator set cooperative frequency modulation method

Abstract

This invention provides a method for coordinated frequency regulation of wide-head hydro-turbine generator units, aiming to solve the problems of PID control parameters being difficult to adapt to wide head variations, difficulty in coordinating the regulation rate and accuracy of AGC frequency regulation commands, and the lack of an overall optimization strategy for multi-unit coordinated frequency regulation. The method includes: acquiring grid frequency signals and power deviation signals; using wavelet transform to extract time-frequency features and adaptively adjusting the gain coefficient; establishing a parameter library covering multiple operating zones and wide head conditions for optimal PID parameter matching; and using a fast convergence control algorithm and a variable gain acceleration program to improve the convergence characteristics of low-load regulation. This application can significantly improve the frequency regulation response speed, achieve coordinated optimization of regulation accuracy and rate, establish an adaptive PID parameter library to adapt to wide head variation conditions, fully utilize the overall potential of the hydro-turbine group for multi-unit coordinated frequency regulation, and ensure long-term frequency regulation performance through a continuous optimization mechanism.

Description

Technical Field

[0001] This invention relates to the field of hydropower technology, specifically to a method for coordinated frequency regulation of wide-head hydro-turbine generator units. Background Technology

[0002] Power system frequency stability is one of the core indicators of safe grid operation. Hydropower generating units, with their inherent characteristics of rapid start-up and shutdown and fast response, occupy a crucial position in grid frequency regulation ancillary services. With the gradual improvement of the frequency regulation ancillary service market in the southern regional power grid, the grid has imposed more stringent performance evaluation standards on generating units participating in frequency regulation services and established corresponding revenue compensation mechanisms. As a vital supporting power source for grid frequency regulation, the frequency regulation performance of large hydropower generating units directly affects the quality of grid frequency regulation and the economic benefits of the generating units.

[0003] The core of frequency regulation control for hydro-generator units lies in the rapid response and precise execution of Automatic Generation Control (AGC) commands. The AGC system issues active power adjustment commands to each generator unit based on grid frequency deviation and tie-line power deviation. The units then adjust the turbine guide vane opening to change the power output, achieving real-time compensation for grid frequency. In actual operation, the units face complex operating conditions, including transitions between different load ranges and variations in operating conditions due to wide head changes. These factors place extremely high demands on the adaptability and robustness of the frequency regulation control system.

[0004] Existing frequency control methods for hydro-generator units have significant shortcomings. First, the tuning of PID control parameters is primarily designed for typical operating conditions, making it difficult to adapt to complex changes under multiple operating zones and wide head conditions. This leads to large regulation errors when using the same parameters at different loads and heads, severely impacting frequency control accuracy. Second, existing technologies lack effective means to optimize the regulation rate of AGC (Automatic Guided Vehicle) commands at low loads in conjunction with regulation accuracy. Convergence characteristics are unsatisfactory, easily resulting in overshoot or undershoot, making it difficult to simultaneously meet the dual requirements of regulation rate and accuracy in the frequency control ancillary services market. Furthermore, current frequency control primarily focuses on individual unit response, lacking an overall optimization strategy for multi-unit coordinated frequency control, thus failing to fully leverage the overall frequency control potential of the hydro-generator group. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art, and provides a method for coordinated frequency regulation of wide-head hydro-turbine generator sets.

[0006] This invention provides a method for coordinated frequency regulation of a wide-head hydro-turbine generator set, comprising: The system inputs are simultaneously collected from the power grid frequency signal, tie line power signal, unit active power feedback signal, guide vane opening feedback signal, and turbine head signal. The system determines whether the current mode is automatic or planned based on the AGC regulation mode signal, and identifies the operating condition by combining the head signal, current load signal, and guide vane opening signal. Wavelet transform is used to extract the time-frequency characteristics of the power grid frequency signal to identify the type of frequency disturbance, and the gain coefficient of the secondary frequency modulation command is adaptively adjusted accordingly. At the same time, variable gain control is implemented on the power command according to the magnitude of the active power deviation. Based on the head signal, current load signal and guide vane opening signal, the optimal PID parameter combination under the current operating condition is matched in the pre-built parameter library. The parameter combination includes proportional gain, integral time and derivative time. A fast convergence control algorithm is used at the end of the active PID regulation of the unit's LCU to perform small load regulation control, and the power deviation change rate is monitored in real time. When the change rate is lower than the set threshold, the variable gain acceleration program is started. By combining the response characteristics and current operating status of each participating frequency regulation unit, frequency regulation tasks are dynamically allocated among multiple units through a collaborative optimization algorithm; The adjustment rate, adjustment accuracy, and response time during the frequency modulation process are evaluated in real time, and the gain coefficient and PID parameters are dynamically optimized based on the evaluation results.

[0007] Optionally, the synchronously acquired power grid frequency signal, power signal, and water head signal meet preset sampling frequency, synchronization error, and timeliness requirements to ensure the real-time performance and consistency of the data input.

[0008] Optionally, the operating condition identification divides the water head into high head zone, medium head zone and low head zone, and the load into low load zone, medium load zone and high load zone, and combines the guide vane opening to assist in judging the unit's operating status.

[0009] Optionally, the frequency disturbance types include step disturbances, ramp disturbances, and oscillation disturbances. Corresponding gain adjustment strategies are set for different disturbance types, and independent gain adjustment coefficient sets are used in automatic mode and planned mode.

[0010] Optionally, the parameter library is constructed through field tests, covering typical operating conditions under various combinations of head and load. Each operating condition corresponds to a set of optimized and tuned PID parameters. During the parameter matching process, an interpolation algorithm is used to achieve a smooth transition between adjacent operating conditions.

[0011] Optionally, the PID parameter switching adopts a gradual transition method to avoid adjustment shock caused by sudden parameter changes and ensure the smoothness of the control process.

[0012] Optionally, the fast convergence control algorithm is based on the predictive control principle, uses historical power deviation data to predict the trend of deviation changes, and corrects the control output in advance to shorten the convergence time.

[0013] Optionally, the variable gain acceleration program is triggered when the power deviation change rate is continuously lower than the threshold, and its gain coefficient and duration are dynamically adjusted according to the deviation convergence until the deviation enters the stable range.

[0014] Optionally, the multi-unit coordinated frequency regulation task allocation algorithm comprehensively considers the response time, available regulation capacity, current head and load range of each unit, adopts the weighted fairness principle for task allocation, and prioritizes scheduling the unit with the best regulation performance.

[0015] Optionally, the frequency modulation performance evaluation module calculates the adjustment rate, adjustment accuracy, and response time in real time, and the real-time optimization module dynamically adjusts the gain coefficient and PID parameters proportionally based on the evaluation results, with the adjustment cycle synchronized with the evaluation cycle.

[0016] The wide-head turbine-generator unit coordinated frequency regulation method of this invention, through a secondary frequency regulation command gain adjustment program and a variable gain control method, adaptively adjusts the gain coefficient according to the type of frequency disturbance, effectively solving the problem of insufficient regulation rate of AGC low-load frequency regulation commands. The regulation response time is significantly shortened compared with traditional methods, meeting the stringent assessment requirements of the power grid frequency regulation ancillary service market. Through a fast convergence control algorithm and a variable gain acceleration program, the regulation rate is improved while maintaining regulation accuracy, avoiding the overshoot or undershoot phenomena common in traditional methods. The regulation accuracy index is significantly improved, meeting the dual assessment requirements of regulation rate and regulation accuracy in the frequency regulation market. Through a PID parameter adaptive method, a parameter library covering multiple operating areas and wide head conditions is established and intelligent matching is achieved, solving the problem that traditional PID parameter tuning is only designed for typical operating conditions. It can maintain excellent frequency regulation performance under wide head variation conditions, and the regulation error is significantly reduced.

[0017] Furthermore, by employing a multi-unit coordinated frequency regulation task allocation strategy, which comprehensively considers the response characteristics and operating status of each unit for optimized allocation, overall coordinated control of the hydropower group was achieved. This fully leverages the advantages of the hydropower group's rapid start-up and shutdown and fast response speed, significantly improving the frequency regulation quality of the regional power grid. Through a frequency regulation performance evaluation and real-time optimization module, key performance indicators are continuously monitored and control parameters are dynamically adjusted, establishing a performance optimization closed loop to ensure optimal frequency regulation conditions are maintained throughout long-term operation. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention; Figure 2 This is a schematic diagram of the core principle framework of the wavelet transform-based frequency disturbance identification and adaptive gain adjustment of the present invention; Figure 3 This is a flowchart illustrating the logical flow of the AGC adjustment mode determination and wide-range head condition identification of the present invention. Figure 4 This is a flowchart of the adaptive method for multi-unit PID control parameters based on parameter library matching, as described in this invention. Figure 5 This is a schematic diagram illustrating the interactive relationship between multi-unit collaborative frequency regulation task allocation and real-time performance optimization in this invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] Unless otherwise specifically stated, the technical or scientific terms used in the embodiments of this invention should be understood in their ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains. The terms "comprising" or "including," as used in the embodiments of this invention, do not limit the shapes, numbers, steps, actions, operations, components, elements, and / or groups thereof mentioned, nor do they exclude the appearance or addition of one or more other different shapes, numbers, steps, actions, operations, components, elements, and / or groups thereof, or the inclusion of these.

[0022] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale, and techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail; however, where appropriate, the illustrated techniques, methods, and apparatus should be considered part of the specification. In all the examples shown and discussed herein, any other specific example may have different values. It should be noted that similar symbols and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0023] In the description of the embodiments of the present invention, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In the embodiments of the present invention, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in the embodiments of the present invention, as well as the features of different embodiments or examples.

[0024] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0025] Please refer to Figures 1 to 5 This invention provides a method for coordinated frequency regulation of wide-head hydro-turbine generator units. This method achieves efficient frequency regulation of a hydro-turbine group under wide-head variation conditions through multi-stage coordinated control. The overall method includes core stages such as data acquisition, operating condition identification, adaptive gain adjustment, PID parameter adaptation, fast convergence control, multi-unit coordinated allocation, and performance evaluation and optimization. The following provides a detailed technical description of each step.

[0026] In step 1, the system synchronously acquires the grid frequency signal, tie-line power signal, unit active power feedback signal, guide vane opening feedback signal, and turbine head signal as system inputs. The grid frequency signal is obtained by a frequency measurement device installed on the power plant side or the grid side. The measurement device uses a high-precision quartz crystal oscillator as the time base, and the sampling frequency is set to no less than 1000 sampling points per second to meet the measurement requirements of rapid frequency signal changes. The tie-line power signal is obtained by a power transmitter installed on the tie-line. The voltage input terminal of the power transmitter is connected to the secondary side of the voltage transformer, and the current input terminal is connected to the secondary side of the current transformer. The transmitter output is a DC signal linearly proportional to the tie-line active power, and the signal range is set according to the rated capacity of the tie-line. The active power feedback signal comes from the unit power measurement loop. The measurement loop uses a three-phase power transmitter to synchronously sample the three-phase voltage and three-phase current at the unit outlet, and calculates the instantaneous value of the unit's active power with a measurement accuracy of no less than 0.2%. The guide vane opening feedback signal is provided by a guide vane position sensor, which is either an angular displacement sensor or a linear displacement sensor. This sensor is installed on the turbine's guide vane mechanism and connected to the guide vane servo piston rod via a mechanical linkage. It converts the guide vane opening into an electrical signal output, with the signal range corresponding to the guide vane's fully closed to fully open position. The head signal is provided by a water level difference measurement device, which includes upstream and downstream water level sensors and a water level difference calculation module. The water level sensors are either pressure level gauges or ultrasonic level gauges, with a measurement error controlled within ±1 cm. The head signal update cycle is set to no more than 1 second. All the above signals are filtered, amplified, and converted from analog to digital by their respective signal conditioning circuits before being synchronously acquired by the data acquisition module. The data acquisition module uses a multi-channel synchronous sampling ADC, with the sampling clock provided by a unified high-precision clock source to ensure strict synchronization of sampling times for each channel. The synchronization error between the power deviation signal and the active power feedback signal does not exceed 10 milliseconds.

[0027] In step 2, the system determines whether the current regulation mode is automatic or planned based on the AGC regulation mode signal. In automatic mode, the regulation command is automatically calculated and generated by the AGC system based on the grid frequency deviation and tie-line power deviation, and then sent to the power plant for automatic execution. After receiving the command, the power plant automatically adjusts the power according to the preset program without manual intervention. In planned mode, the power plant executes power output according to the pre-programmed power generation plan curve. The regulation command is calculated and generated based on the deviation between the actual load and the planned value, and the regulation process follows the predetermined schedule. The system simultaneously collects turbine head signals, current load signals, and guide vane opening signals for operating condition identification. The operating condition identification module divides the unit's operating range into three zones based on the head value: the high head zone corresponds to a head value between 90% and 100% of the rated head, the medium head zone corresponds to a head value between 60% and 90% of the rated head, and the low head zone corresponds to a head value between 40% and 60% of the rated head. The efficiency characteristics, power response characteristics, and stability boundaries of turbines vary significantly across different head ranges, and the operating condition identification results directly affect the selection of subsequent control parameters. Load range division is determined based on the ratio of the unit's current active power to its rated power: low load range corresponds to a load rate of 30% to 50%, medium load range to 50% to 80%, and high load range to 80% to 100%. The guide vane opening signal is used to assist in judging the unit's operating status. When the guide vane opening is below 20%, it indicates that the unit is in a low-opening operating range, where the turbine's nonlinear characteristics are more prominent, requiring special attention to the adaptability of control parameters. The output of the operating condition identification is a combination of the current head zone number and the load zone number, which serves as the key index for parameter library retrieval in step 4.

[0028] In step 3, the system uses wavelet transform to extract the time-frequency features of the power grid frequency signal to identify the type of frequency disturbance. The wavelet transform employs a preset wavelet basis function for multi-level decomposition to extract the time-frequency features of the frequency signal. The selected wavelet basis function is the db4 wavelet, and the number of decomposition levels is set to 5. The original frequency signal, after preprocessing, is input into the wavelet decomposition module. In each level of decomposition, the signal is divided into low-frequency approximate components and high-frequency detail components. The high-frequency components obtained from each level contain abrupt changes and transient features in the frequency signal. By calculating the energy distribution of the high-frequency components at each level, the disturbance type can be determined. When the energy of the highest-level high-frequency component is significantly higher than that of other levels, it indicates the presence of rapidly changing step components in the frequency signal, and is identified as a step disturbance. When the energy distribution of the high-frequency components at each level is relatively uniform and exhibits a certain periodicity, it indicates that the frequency signal exhibits a gradually changing ramp characteristic, and is identified as a ramp disturbance. When one or more levels of high-frequency components exhibit obvious periodic oscillation characteristics, it is identified as an oscillating disturbance. The frequency disturbance type identification result is used for subsequent adaptive adjustment of the gain coefficient. For step disturbances, the gain coefficient is set to 1.5 to 2.0 times the normal value to achieve a fast response; for ramp disturbances, the gain coefficient is set to 1.2 to 1.5 times the normal value to balance response speed and control stability; for oscillation disturbances, the gain coefficient is set to 0.8 to 1.0 times the normal value to suppress overshoot. The system also employs a variable gain control method for active power commands, dynamically adjusting the gain coefficient according to the power deviation to accelerate response speed. The variable gain control module calculates the deviation between the active power command value and the current active power feedback value in real time. When the deviation is greater than 5% of the unit's rated power, the gain coefficient is set to 1.3 times to achieve a faster tracking speed; when the deviation is between 2% and 5% of the unit's rated power, the gain coefficient is set to 1.1 times to balance response speed and stability; when the deviation is less than 2% of the unit's rated power, the gain coefficient returns to 1.0 times to avoid overshoot. The secondary frequency modulation command gain adjustment program sets independent gain adjustment coefficients for automatic mode and planned mode respectively. The gain coefficient in automatic mode is set to the range of 1.2 to 1.8 times, and the gain coefficient in planned mode is set to the range of 1.1 to 1.5 times. The program automatically switches the corresponding gain parameter set according to the adjustment mode determined in step 2.

[0029] In step 4, the system, based on the collected head signal, current load signal, and guide vane opening signal, performs optimal PID parameter matching in a pre-built parameter library to determine the proportional gain, integral time, and derivative time parameter combination for the current operating condition. The parameter library is established through field tests, covering multiple typical operating conditions across various load operating ranges in high-head, medium-head, and low-head zones. The parameter library construction process is as follows: PID parameter optimization tests are conducted at typical operating points for each head zone and each load zone combination. During the tests, the head is kept stable at the value corresponding to the test operating point. The dynamic response characteristics of the unit are obtained through step response testing. Then, the Ziegler-Nichols method or the critical proportional gain method is used for initial PID parameter tuning. Based on this, fine-tuning is performed to obtain the optimal PID parameter combination for that operating point. Each optimal parameter combination includes three parameters: proportional gain Kp, integral time Ti, and derivative time Td. The total capacity of the parameter library is no less than 100 parameter combinations, covering parameter configurations for three head zones, three load zones, and multiple typical operating points within each zone. The parameter library is organized using a two-dimensional lookup table structure. Row indices are head zone numbers, and column indices are load zone numbers. Each entry stores the optimal PID parameter combination for the corresponding operating point, as well as the head boundary value and load rate boundary value for that operating point. During parameter matching, the parameter matching module determines the head zone number based on the current head value and the load zone number based on the current load rate. Then, it determines the final parameters according to the following rules: if the current operating condition corresponds exactly to a typical operating point in the parameter library, the parameters for that operating point are directly used; if the current operating condition lies between two adjacent typical operating points, an interpolation algorithm is used to perform linear interpolation between the parameters of the two adjacent points to obtain smoothly transitioning parameter values. The interpolation algorithm uses a bilinear interpolation method, first interpolating in the head direction and then in the load direction, or vice versa, ultimately obtaining a smoothly transitioning parameter combination. The proportional gain Kp is set within a range of 0.5 to 5.0, the integral time Ti within a range of 1.0 to 20.0 seconds, and the derivative time Td within a range of 0.1 to 2.0 seconds. A gradual transition is used when switching parameters to avoid adjustment shock. The transition time is set within the range of 5 to 15 seconds. During the transition, the PID parameters gradually change from their original values ​​to the new values, and the change process uses exponential smoothing to ensure smooth switching.

[0030] In step 5, the system employs a fast convergence control algorithm to achieve low-load regulation control at the active power PID control terminal of the unit's LCU. Simultaneously, it monitors the power deviation rate of change in real time. When the rate of change falls below a set threshold, a variable gain acceleration program is initiated to improve convergence characteristics. The fast convergence control algorithm is based on predictive control principles. It uses power deviation data from the previous few sampling periods to predict the deviation trend and adjusts the control output in advance based on the prediction results to shorten the convergence time. The predictive control module maintains a sliding window of length N to store power deviation data from the most recent N sampling periods. The value of N is determined based on the system response speed and computational resources, typically ranging from 10 to 20. The predictive algorithm uses a linear extrapolation method. It calculates the deviation rate of change based on the temporal trend of the deviation data within the sliding window, and then extrapolates the time point at which the deviation will enter the zero-deviation interval after several sampling periods. Based on this, the PID control output is corrected in advance to accelerate the convergence process. The correction amount of the control output is proportional to the predicted convergence time and the current deviation amplitude, and the correction direction is opposite to the deviation direction. The threshold for low-load regulation control is between 1% and 5% of the unit's rated power. When the power deviation falls within this range, the low-load regulation control mode is activated. In this mode, traditional PID control may have insufficient response sensitivity. The fast convergence control algorithm enhances the response capability to small deviations through the aforementioned prediction and correction mechanism. The trigger condition for the variable gain acceleration program is that the power deviation change rate is less than 0.5% of the rated power per second. When the power deviation change rate is detected to be below this threshold for more than three consecutive sampling periods, it indicates that the current regulation process has entered a slow convergence stage, at which point the variable gain acceleration program is activated. The acceleration gain coefficient is set to a range of 1.5 to 2.5 times. Under the action of the acceleration program, the controller's regulation effect is enhanced to accelerate the convergence speed. The duration of the acceleration program is dynamically adjusted according to the deviation convergence situation. When the deviation change rate is detected to return to the normal level or the deviation has entered the stable range, the acceleration program is exited and the normal gain is restored. The criteria for determining the stable range are that the absolute value of the power deviation is less than 0.5% of the rated power and the absolute value of the deviation change rate is less than 0.1% of the rated power per second.

[0031] In step 6, the system comprehensively analyzes the response characteristics and current operating status of each unit participating in frequency regulation, and allocates frequency regulation tasks among the units based on a collaborative optimization algorithm to achieve rapid restoration of the grid frequency. The multi-unit collaborative frequency regulation task allocation algorithm comprehensively considers factors such as the response time, regulation capacity, current head, and load operating range of each unit. The response time factor is characterized by the response time constant of each unit, defined as the time required from issuing the frequency regulation command to the unit's active power first reaching 50% of the target deviation range. Units with smaller response time constants have higher task allocation priority in rapid frequency regulation scenarios. The regulation capacity factor is characterized by the current available frequency regulation capacity of each unit, which is the difference between the unit's maximum adjustable power and its current actual power, considering both upward and downward adjustable capacity. Units with larger current available frequency regulation capacity have greater task-bearing capacity. The current head and load operating range factors are characterized by the unit's current operating condition zone. Units in the high-efficiency operating zone have higher regulation efficiency and economy when head and load conditions are good, and should be appropriately favored in task allocation. The collaborative optimization algorithm adopts a weighted fair allocation principle. The task allocation model takes the shortest grid frequency recovery time as the objective function and the available frequency regulation capacity and regulation rate of each unit as constraints. The frequency regulation task allocation ratio of each unit is obtained by solving this optimization problem. Let M be the number of units participating in frequency regulation, and let the allocation coefficient of the i-th unit be... ,satisfy Normalization constraints, The solution formula is as follows:

[0032] in For the first Response time factor of the Taiwanese unit. For the first Available frequency modulation capacity factor for the Taiwanese unit and The weighted index is used to adjust the relative weights of response time and regulation capacity in allocation decisions, especially when the grid frequency deviation is large and a rapid response is required. The algorithm prioritizes units with faster response times by selecting the largest β value. When the grid frequency deviation is small and continuous stable regulation is required, a larger β value is selected to prioritize units with sufficient regulation capacity. This allocation algorithm ensures a balanced frequency regulation load for each unit, while prioritizing units with optimal regulation performance for deep frequency regulation. The coordinated frequency regulation strategy also includes a load allocation optimization module. This module optimizes the power allocation ratio of each unit based on its economic and frequency regulation performance differences, while meeting the grid frequency regulation requirements, to reduce overall energy consumption. The economic differences are mainly reflected in the different water consumption per unit power of units under different head conditions. The load allocation optimization module prioritizes units in the high-efficiency zone to undertake frequency regulation tasks to reduce water consumption.

[0033] In step 7, the system monitors performance indicators such as adjustment rate, adjustment accuracy, and response time in real time during the frequency regulation process. After comparing these indicators with preset standards, the system dynamically adjusts control parameters to achieve continuous optimization. The frequency regulation performance evaluation module calculates the following three types of indicators in real time: the adjustment rate indicator has a calculation period of 60 seconds and is defined as the ratio of the actual active power change of the unit to the corresponding time span within the calculation period, reflecting the unit's ability to track changes in the frequency regulation command; the adjustment accuracy indicator's evaluation interval is the average power deviation within 120 seconds after the adjustment command is issued, and is defined as the average absolute value of the deviation between the actual power and the commanded power during this period, reflecting the unit's ability to maintain the target power; the response time indicator is defined as the time from the issuance of the command to the first time the power deviation enters the stable range. The criterion for determining the stable range is that the absolute value of the power deviation is less than 1% of the rated power for three consecutive seconds. The real-time optimization module dynamically adjusts the gain coefficient in step 3 and the PID parameters in step 4 based on the performance evaluation results. The adjustment logic is as follows: When the adjustment rate index is lower than 90% of the preset standard, the gain coefficient in step 3 is increased by 5% to 10% of the current value; when the adjustment accuracy index is higher than 110% of the preset standard, the proportional gain in the PID parameters in step 4 is reduced by 3% to 5% of the current value to reduce overshoot; when the response time index exceeds 110% of the preset standard, the trigger threshold of the variable gain acceleration program in step 5 is increased by 10% to 15% of the current value to start the acceleration program earlier. The adjustment range is set within a certain percentage range of the current parameters according to the degree of deviation to avoid system instability caused by excessive adjustment in a single instance. The adjustment cycle is synchronized with the performance evaluation cycle, executed once every 60 seconds to ensure that parameter adjustment and performance evaluation remain synchronized.

[0034] This invention also includes a field test verification phase, which verifies the secondary frequency regulation command gain adjustment program, fast convergence control algorithm, and PID parameter adaptive program for improving the regulation rate through field tests. The field test scheme is designed as follows: First, each program and algorithm is independently tested under single-unit conditions, maintaining stable unit operation during testing, and the unit's response characteristics are observed by simulating AGC frequency regulation command injection; then, an integrated test is conducted to verify the correctness and stability of the collaborative work of each module; finally, a practical frequency regulation service assessment is performed, conducting a 72-hour continuous operation test while meeting the assessment requirements of the Southern Regional Power Grid Frequency Regulation Ancillary Service Market. Test data acquisition includes grid frequency signals, unit active power, guide vane opening, PID control output, and various intermediate calculation variables, with a data sampling frequency of no less than 100 sampling points per second. The test data is compared with preset assessment standards to verify the effectiveness and stability of each program and algorithm. The assessment standards are formulated based on the technical requirements of the Southern Regional Power Grid Frequency Regulation Ancillary Service Market, including lower limits for frequency regulation rate indicators, frequency regulation accuracy indicators, and response time indicators. The frequency regulation revenue assessment module calculates the frequency regulation revenue after implementing the method of this invention, based on the assessment standards and revenue mechanisms of the frequency regulation ancillary service market in the Southern Regional Power Grid. The revenue calculation model converts frequency regulation performance indicators into corresponding revenue coefficients, accumulates the frequency regulation revenue for each time period to obtain daily and monthly revenue, and compares the optimized frequency regulation revenue with historical revenue data before optimization to quantify the improvement effect.

[0035] The implementation process of the method of this invention is illustrated below with a specific application example. Assume a hydropower station has four 300 MW turbine-generator units participating in grid frequency regulation services. The current operating conditions are: Unit 1 is in the high head zone with a load factor of 65%; Unit 2 is in the medium head zone with a load factor of 75%; Unit 3 is in the low head zone with a load factor of 55%; and Unit 4 is in the high head zone with a load factor of 85%. The system receives a frequency regulation command from the AGC (Automatic Generation Control) system, requiring each unit to increase its active power by 10 MW within 15 seconds to respond to the grid frequency drop. First, in step 1, the system synchronously collects the operating parameters of the four units. In step 2, it completes the AGC regulation mode judgment and operating condition identification, confirming that Unit 1 is in the high head medium load zone, Unit 2 is in the medium head high load zone, Unit 3 is in the low head medium load zone, and Unit 4 is in the high head high load zone. In step 3, the system identifies the frequency disturbance type as a step disturbance using wavelet transform, and accordingly sets the gain coefficient to 1.6 times the normal value. Simultaneously, based on the judgment that the current power deviation of each unit has entered the variable gain control range, the system determines the appropriate gain multiple for each unit. In step 4, the system performs PID parameter matching in the parameter library according to the operating condition zones of each unit. Unit 1 is matched with Kp=2.3, Ti=8.5 seconds, and Td=0.8 seconds; Unit 2 with Kp=2.8, Ti=6.2 seconds, and Td=0.6 seconds; Unit 3 with Kp=1.9, Ti=12.0 seconds, and Td=1.2 seconds; and Unit 4 with Kp=3.2, Ti=5.0 seconds, and Td=0.5 seconds. In step 5, the fast convergence control algorithm is activated and the power deviation change rate is monitored. Due to the large amplitude of the frequency modulation command, the system determines that the deviation change rate is within the normal range and does not activate the variable gain acceleration program. In step 6, the collaborative optimization algorithm calculates the task allocation coefficients for each unit. Considering that units 2 and 4 have shorter response times and are in their high-efficiency operating range, their allocation coefficients are 0.30 and 0.32, respectively. The allocation coefficients for units 1 and 3 are 0.22 and 0.16, respectively. After allocation, each unit needs to increase its power by 2.2 MW, 3.0 MW, 1.6 MW, and 3.2 MW, respectively. In step 7, the system monitors frequency regulation performance in real time. The calculations show that the regulation rate of each unit reaches at least 0.8% / second of the rated power, the regulation accuracy is controlled within 1.5% of the rated power, and the response time is less than 10 seconds, meeting the frequency regulation service assessment standards. Through the application of this invention, the average monthly frequency regulation revenue of the hydropower station increases by approximately 25% compared to before optimization, and the frequency regulation performance indicators reach the first-tier assessment standard of the frequency regulation ancillary service market in the southern regional power grid.

[0036] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to another specific embodiment.

[0037] This embodiment focuses on the specific application of the method of the present invention in a wide-head variable-amplitude hydropower station. This hydropower station is equipped with six pumped-storage units with a rated power of 250 MW each, and the head variation range is 80 meters to 180 meters, making it a typical wide-head hydropower station. This station participates in the secondary frequency regulation service of the power grid, undertaking both peak shaving and frequency regulation tasks. Its operating conditions are complex and variable, placing higher demands on the frequency regulation control strategy.

[0038] In the data acquisition phase, the power station is equipped with a high-precision data acquisition system. The grid frequency signal sampling frequency is set to 2000 sampling points per second to meet the measurement requirements for rapid response of the pumped storage units. Power measurement uses a 0.1-class precision three-phase power transmitter, and the synchronization error of the active power feedback signal is controlled within 5 milliseconds. Head measurement uses an ultrasonic level gauge, with both upstream and downstream water level measurement errors less than ±5 mm. The head signal update cycle is set to 0.5 seconds to meet the monitoring requirements of scenarios with rapid changes in head.

[0039] In the operating condition identification phase, considering the operating characteristics of pumped storage units, the operating condition zones have been more finely divided. The head zone, based on the original three-zone division, adds an ultra-high head zone and a water level fluctuation zone. The ultra-high head zone corresponds to a head value between 100% and 110% of the rated head, while the water level fluctuation zone corresponds to a head value between 35% and 45% of the rated head. The load zone, based on the original three-zone division, adds an ultra-low load zone to accommodate the operating requirements of pumped storage units under both power generation and pumping phase regulation conditions. The operating condition identification module also integrates a turbine operating area judgment function. Based on the guide vane opening and head data, it calculates the turbine's specific speed coefficient to determine whether the turbine is currently operating in its high-efficiency zone, thus providing a more refined adjustment strategy in the PID parameter adaptive stage.

[0040] In the gain adjustment stage, this embodiment employs differentiated gain adjustment strategies for different operating conditions. Under power generation conditions, the secondary frequency regulation command gain adjustment program uses an asymmetric gain coefficient based on the direction and amplitude of the frequency regulation command. The gain coefficient for the upward adjustment command is set to 1.3 to 1.6 times, and the gain coefficient for the downward adjustment command is set to 1.2 to 1.5 times. This is because the power regulation of the hydro-generator unit under turbine conditions mainly relies on changes in the guide vane opening; the response speed when the guide vane opening decreases is usually faster than when it increases. Under pumping conditions, the gain adjustment program uses a different parameter set, with corresponding gain coefficient ranges set for the upward and downward pumping commands. Frequency disturbance identification uses an improved wavelet transform method. Based on the original db4 wavelet, a wavelet denoising preprocessing stage is added to improve the signal-to-noise ratio of the frequency signal, and the number of decomposition layers is increased to 7 to capture higher-frequency disturbance components.

[0041] In the PID parameter adaptive stage, the parameter library capacity in this embodiment is expanded to 300 parameter combinations to adapt to the more complex operating conditions of pumped storage units. The parameter library is organized according to three dimensions: head zone, load zone, and operating condition. The operating condition dimension distinguishes between power generation, pumping, power generation phasing, and pumping phasing conditions. Parameter matching adopts a three-level index structure. First, the main zones of the parameter library are determined based on the operating conditions. Then, secondary filtering is performed based on the head zone. Finally, precise matching is performed based on the load zone. The parameter interpolation algorithm uses cubic spline interpolation to obtain a smoother parameter transition curve, with the transition time set to the range of 8 to 20 seconds. The parameter adaptive module also integrates a parameter learning function, automatically recording the frequency regulation effect data under the current operating condition after each frequency regulation task is completed, and periodically updating the parameter library to improve the accuracy of parameter matching.

[0042] In the fast convergence control phase, the predictive control algorithm in this embodiment employs an adaptive prediction window length mechanism, dynamically adjusting the prediction window length based on the system's current response characteristics. When the system is in a fast response state, the prediction window is shortened to improve response speed; when the system is in a slow convergence state, the prediction window is lengthened to improve prediction accuracy. The trigger threshold for the variable gain acceleration program is adaptively adjusted based on the characteristics of the current frequency modulation task. A lower trigger threshold is used for large-amplitude frequency modulation commands to initiate the acceleration program earlier, while a higher trigger threshold is used for small-amplitude frequency modulation commands to avoid unnecessary acceleration.

[0043] In the multi-unit coordinated frequency regulation stage, the coordinated control of the six generating units in this embodiment adopts a hierarchical optimization architecture. The first layer is the station-wide coordinated optimization layer, which comprehensively analyzes the overall operating status and available frequency regulation resources of the six units to formulate a station-wide frequency regulation strategy. The second layer is the unit group coordinated optimization layer, which divides the six units into two groups for coordinated optimization, with each group containing three units. The intra-group coordinated optimization uses a distributed control algorithm to reduce computational complexity. The third layer is the single-unit optimization layer, where each unit performs independent closed-loop control according to its assigned tasks. The allocation of station-wide frequency regulation tasks adopts a model predictive control method, with the minimization of the sum of squared errors of the station-wide frequency regulation response as the objective function. The real-time allocation coefficients of each unit are obtained through rolling optimization. The load allocation optimization module integrates the unit efficiency curve model, calculates the water consumption rate of each unit in real time, and prioritizes the scheduling of units with low water consumption rates to reduce station-wide energy consumption while meeting frequency regulation requirements.

[0044] In the performance evaluation and optimization phase, this embodiment establishes a multi-dimensional performance evaluation system. In addition to the three basic indicators—regulation rate, regulation accuracy, and response time—it also adds frequency regulation energy consumption and frequency regulation stability indicators. The frequency regulation energy consumption indicator is defined as the ratio of water consumed to the unit's rated power, reflecting the economy of the frequency regulation process. The frequency regulation stability indicator is defined as the number of times and duration the power deviation exceeds the stable range during frequency regulation, reflecting the smoothness of the frequency regulation process. The real-time optimization module adjusts comprehensive parameters based on the multi-dimensional evaluation results. When the economic indicators deviate from the target, the gain coefficient is adjusted first to balance response speed and energy consumption; when the stability indicators deviate from the target, the PID parameters are adjusted first to enhance system damping.

[0045] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for coordinated frequency regulation of a wide-head hydro-turbine generator unit, characterized in that, include: The system inputs are simultaneously collected from the power grid frequency signal, tie line power signal, unit active power feedback signal, guide vane opening feedback signal, and turbine head signal. The system determines whether the current mode is automatic or planned based on the AGC regulation mode signal, and identifies the operating condition by combining the head signal, current load signal, and guide vane opening signal. Wavelet transform is used to extract the time-frequency characteristics of the power grid frequency signal to identify the type of frequency disturbance, and the gain coefficient of the secondary frequency modulation command is adaptively adjusted accordingly. At the same time, variable gain control is implemented on the power command according to the magnitude of the active power deviation. Based on the head signal, current load signal and guide vane opening signal, the optimal PID parameter combination under the current operating condition is matched in the pre-built parameter library. The parameter combination includes proportional gain, integral time and derivative time. A fast convergence control algorithm is used at the end of the active PID regulation of the unit's LCU to perform small load regulation control, and the power deviation change rate is monitored in real time. When the change rate is lower than the set threshold, the variable gain acceleration program is started. By combining the response characteristics and current operating status of each participating frequency regulation unit, frequency regulation tasks are dynamically allocated among multiple units through a collaborative optimization algorithm; The adjustment rate, adjustment accuracy, and response time during the frequency modulation process are evaluated in real time, and the gain coefficient and PID parameters are dynamically optimized based on the evaluation results.

2. The wide-head turbine-generator unit coordinated frequency regulation method according to claim 1, characterized in that, The synchronously acquired power grid frequency signal, power signal, and water head signal meet the preset sampling frequency, synchronization error, and timeliness requirements to ensure the real-time performance and consistency of the data input.

3. The wide-head turbine-generator unit coordinated frequency regulation method according to claim 1, characterized in that, The operating condition identification divides the water head into high head, medium head and low head zones, and the load into low load, medium load and high load zones, and combines the guide vane opening to assist in judging the unit's operating status.

4. The wide-head turbine-generator unit coordinated frequency regulation method according to any one of claims 1 to 3, characterized in that, The frequency disturbance types include step disturbance, ramp disturbance and oscillation disturbance. Corresponding gain adjustment strategies are set for different disturbance types, and independent gain adjustment coefficient sets are used in automatic mode and planned mode.

5. The wide-head turbine-generator unit coordinated frequency regulation method according to any one of claims 1 to 3, characterized in that, The parameter library was constructed through field tests, covering typical operating conditions under various combinations of head and load. Each operating condition corresponds to a set of optimized PID parameters. During the parameter matching process, an interpolation algorithm is used to achieve a smooth transition between adjacent operating conditions.

6. The method for coordinated frequency regulation of a wide-head hydro-turbine generator unit according to any one of claims 1 to 3, characterized in that, The PID parameter switching adopts a gradual transition method to avoid adjustment shock caused by sudden parameter changes and ensure the smoothness of the control process.

7. The wide-head turbine-generator unit coordinated frequency regulation method according to any one of claims 1 to 3, characterized in that, The fast convergence control algorithm is based on the predictive control principle. It uses historical power deviation data to predict the trend of deviation changes and adjusts the control output in advance to shorten the convergence time.

8. The method for coordinated frequency regulation of a wide-head hydro-turbine generator unit according to any one of claims 1 to 3, characterized in that, The variable gain acceleration program is triggered when the power deviation rate of change is continuously lower than the threshold. Its gain coefficient and duration are dynamically adjusted according to the deviation convergence until the deviation enters the stable range.

9. The method for coordinated frequency regulation of a wide-head hydro-turbine generator unit according to any one of claims 1 to 3, characterized in that, The multi-unit coordinated frequency regulation task allocation algorithm comprehensively considers the response time, available regulation capacity, current head and load range of each unit, adopts the weighted fairness principle for task allocation, and prioritizes scheduling the unit with the best regulation performance.

10. The method for coordinated frequency regulation of a wide-head hydro-turbine generator unit according to any one of claims 1 to 3, characterized in that, The frequency modulation performance evaluation module calculates the adjustment rate, adjustment accuracy and response time in real time. The real-time optimization module dynamically adjusts the gain coefficient and PID parameters proportionally based on the evaluation results, and the adjustment cycle is synchronized with the evaluation cycle.

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