Hydroelectric generating set speed regulator PID parameter optimization method applied to electric power secondary frequency modulation market

Abstract

The invention discloses a hydroelectric generating set speed regulator PID parameter optimization method applied to an electric power secondary frequency modulation market, and relates to the technical field of electric power system automatic control, and the system comprises the following components: S100, data acquisition and preprocessing, S200, PID parameter optimization model establishment, S300, online parameter optimization and adjustment, S400, performance monitoring and feedback, and S500, online parameter optimization and adjustment. And benefit evaluation and optimization iteration are carried out. The optimization algorithm and the closed loop iteration mechanism are introduced, the optimal solution is searched in the parameter space according to the pre-defined evaluation criterion, accurate optimization and continuous adjustment of PID parameters can be achieved, the response speed and accuracy of the hydroelectric generating set in the electric power secondary frequency modulation market are remarkably improved, the frequency stability of a power grid is enhanced, and the power grid performance is improved. By continuously optimizing the PID parameters, the hydroelectric generating set can respond to the frequency modulation instruction of the power grid more efficiently and accurately, and the competitiveness and the operation efficiency of the electricity market are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system automatic control, and in particular to a method for optimizing PID parameters of a hydropower unit speed regulator applied to the power secondary frequency regulation market. Background Art

[0002] In the power system, the secondary frequency regulation market is an important link in ensuring the stability of the power grid frequency. For hydropower units, the performance of their speed regulators directly affects the accuracy and response speed of frequency regulation. With the gradual opening of the power market and the intensification of competition, hydropower units need to respond to the power grid's frequency regulation instructions more efficiently and accurately to improve the competitiveness and operating efficiency of the power market.

[0003] When establishing a PID parameter optimization model for a hydropower unit speed regulator, traditional technology is unable to search for the optimal solution in the parameter space based on pre-defined evaluation criteria. It lacks a closed-loop iterative optimization mechanism and is unable to accurately optimize and continuously adjust the PID parameters based on the unit operating data and economic benefit evaluation results, thus limiting the improvement of unit performance and the enhancement of market competitiveness.

[0004] In summary, traditional technology has many shortcomings in establishing the PID parameter optimization model of the hydropower unit speed regulator, and it is difficult to meet the actual needs of the power secondary frequency regulation market. Therefore, it is particularly important to develop a PID parameter optimization method for the hydropower unit speed regulator applied to the power secondary frequency regulation market. Summary of the invention

[0005] The purpose of the present invention is to make up for the deficiencies of the prior art and provide a PID parameter optimization method for a hydropower unit speed regulator applied to the power secondary frequency regulation market. The method introduces an optimization algorithm and a closed-loop iteration mechanism, sets the initial parameters of the algorithm, and searches for the optimal solution in the parameter space according to predefined evaluation criteria, thereby achieving precise optimization and continuous adjustment of PID parameters to meet the real-time and accuracy requirements of the power secondary frequency regulation market and promote the continuous advancement of power system automatic control technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for optimizing PID parameters of a hydropower unit speed regulator applied to the electric power secondary frequency regulation market, the specific steps of the method are as follows; S100, data collection and preprocessing: collect historical operation data of the unit, including but not limited to AGC instructions, unit output power, speed, head change, and clean and standardize the data; S200, establishing a PID parameter optimization model: according to the dynamic characteristics and control objectives of the AGC system, a mathematical model of the system is established, including but not limited to the input, output, and state equation of the system, and a mathematical model of the PID controller is defined. The output of the PID controller is the control quantity u, which is expressed as: ,in, represents the output control quantity of the controller at time t, represents the proportionality coefficient, Indicates the deviation signal, represents the integral coefficient, represents the integral of the deviation signal from 0 to time t, represents the differential coefficient, It represents the rate of change of the error. The differential term is used to predict the trend of the error change, so as to take measures in advance. According to the objectives and constraints, the optimization objective function is defined, the initial parameters of the algorithm are set, the optimization algorithm is selected, and the optimal solution is searched in the parameter space according to the pre-defined evaluation criteria. Initialize the parameter space within the range of PID parameters, use the optimization algorithm to perform iterative search in the parameter space, calculate the system performance under the current PID parameter combination in each iteration, evaluate the performance of the current PID parameter combination according to the optimization objective function, and update the optimal solution if the performance of the current PID parameter combination is better than the previous optimal solution, check whether the termination condition is met, and if the termination condition is met, stop the iteration and output the optimal PID parameter combination; Apply the optimal PID parameter combination to the actual AGC system, conduct performance tests, collect response time, overshoot, oscillation times and AGC pass rate performance index data, compare the performance index data before and after optimization, analyze the optimization effect, if the optimization effect is not ideal, adjust the optimization objective function or optimization algorithm parameters, re-optimize, further adjust the PID parameters according to the test results, consider using integral saturation limit, differential first and filtering strategies to optimize the performance of the PID controller; S300, online parameter optimization and adjustment: Integrate the above model into the hydropower unit speed governor control system to achieve online dynamic adjustment of PID parameters. According to the real-time received AGC instructions and unit operating status, the model automatically calculates and outputs the optimal PID parameters, so that the unit can respond to AGC instructions quickly and accurately. S400, Performance Monitoring and Feedback: Establish a performance monitoring system to continuously track and record the AGC response of the unit after the optimized PID parameters are adopted. If performance degradation is found through regular evaluation, a new round of parameter optimization process will be automatically triggered; S500, benefit evaluation and optimization iteration: Combined with the frequency regulation market revenue, unit operating efficiency and operation and maintenance cost economic indicators, the comprehensive benefits brought by PID parameter optimization are evaluated. According to the evaluation results, the optimization strategy is continuously adjusted to form a closed-loop iterative optimization mechanism to continuously improve the unit performance and market competitiveness.

[0007] Furthermore, the S100 data collection involves determining the location and access rights of the data source, selecting key fields including but not limited to AGC instructions, unit output power, speed and head changes, and extracting data using SQL queries, data crawling tools or API interfaces. Data cleaning includes processing missing values, detecting and processing outliers, and deduplication. Data standardization processing involves normalization, feature scaling, data conversion, and dividing the data set into training sets, validation sets, and test sets, and performing data quality checks to ensure the integrity, accuracy, and consistency of the data.

[0008] Furthermore, the S200 establishes the initial parameters of the PID parameter optimization model setting algorithm, selects the optimization algorithm, and searches for the optimal solution in the parameter space according to the predefined evaluation criteria, which specifically includes two steps: the first step is the initial parameters of the particle swarm part, the second step is the initial parameters of the ant colony part, and the third step is the optimal solution search process; The first step, the specific contents of the initial parameters of the particle swarm are: Number of particles : Determined by the problem size and computing resources; Inertia Weight : Usually between 0.4 and 0.9, the initial value can be set to , and gradually decreases with iterations; Learning Factor and : Generally between 1 and 2; Particle velocity range and : Determined according to the range of parameter space; Step 2: Initial parameters of the ant colony: Number of ants Can be the same as or different from the number of particles; Pheromone volatility coefficient : Usually between 0.1 and 0.5; Initial value of pheromone intensity Determined by the scale of the problem; Step 3: Searching for the optimal solution: Initialize particles and ants: Random generation The positions and velocities of particles, each particle represents a set of parameter vectors , randomly placed in parameter space Only ants; Calculate fitness: For each particle and ant, calculate the evaluation function As fitness; Particle swarm update: For each particle, update its velocity and position: ,in, and Particles In the The speed and position of the iteration, It is a particle The best location in history, is the best historical position of the entire particle swarm, and is a random number in the interval [0, 1]; Ant colony update: Ants choose the next parameter dimension to move based on pheromone concentration; Update pheromone concentration: ,in is the parameter dimension and The pheromone concentration between is the pheromone increment left by the ants on the path in this iteration; repeat the fitness calculation-particle swarm update-ant colony update until the stopping condition is met.

[0009] Furthermore, the S300 online parameter optimization and adjustment system architecture design covers PID parameter optimization model, hydropower unit speed governor control system, AGC command receiving and unit status monitoring module. A unified data communication interface and protocol are used between the modules. The PID parameter optimization model is embedded in the speed governor control system as an independent calculation module to receive AGC commands and unit status data in real time.

[0010] Furthermore, the S300 online parameter optimization and adjustment includes data reception and processing, PID parameter online optimization, testing and optimization, and operation and maintenance processes. The data receiving module parses the AGC instructions in real time and monitors the unit status, preprocesses key parameters, and the PID parameter optimization model calculates the optimal parameters based on real-time data and outputs them to the speed governor control system. After the system is actually tested, it is iteratively optimized based on the results. During operation, real-time monitoring, data backup and recovery, and regular maintenance and upgrades are implemented.

[0011] Furthermore, the S400 performance monitoring and feedback establishes a performance monitoring system, including data acquisition, data storage, data analysis modules and feedback control modules.

[0012] Furthermore, the S400 performance monitoring and feedback regularly evaluates the unit performance. If the performance does not meet the standard or declines, the parameter optimization process is automatically triggered, and the PID parameter optimization model recalculates and outputs new parameters to the speed governor control system. After the optimization is implemented, the unit operation status is monitored and recorded, and the data is fed back to the system to form a closed-loop iterative optimization.

[0013] Furthermore, in the benefit evaluation stage of the S500 benefit evaluation and optimization iteration, frequency regulation market revenue, unit operating efficiency and operation and maintenance cost are selected as evaluation indicators, the calculation method is refined and data before and after the optimization of PID parameters are collected, benefit calculation and in-depth analysis are performed, and the effectiveness and sustainability of the optimization strategy are evaluated. In the optimization iteration stage, the optimization strategy is adjusted according to the evaluation results, new solutions are proposed and applied to the unit speed governor control system, operating data and economic indicators are continuously monitored, benefits are regularly evaluated, and the PID parameter optimization strategy is continuously adjusted to form a closed-loop iterative mechanism.

[0014] Compared with the existing technology, a method for optimizing the PID parameters of the hydropower unit speed regulator applied to the power secondary frequency regulation market has the following beneficial effects: 1. The present invention introduces an optimization algorithm and a closed-loop iteration mechanism, searches for the optimal solution in the parameter space according to predefined evaluation criteria, and can achieve accurate optimization and continuous adjustment of PID parameters, which significantly improves the response speed and accuracy of hydropower units in the secondary frequency modulation market of electric power, thereby enhancing the frequency stability of the power grid. By continuously optimizing PID parameters, the present invention helps hydropower units respond to the frequency modulation instructions of the power grid more efficiently and accurately, thereby improving the competitiveness and operating efficiency of the power market.

[0015] 2. The present invention forms a closed-loop iterative mechanism by regularly evaluating unit performance and adjusting optimization strategies. This mechanism not only improves the market competitiveness of the unit, but also makes the PID parameter optimization strategy more adaptable to changes in the power market and technological updates through continuous iterative optimization. In addition, the method proposed in the present invention also reduces the operation and maintenance costs of the unit, improves the operating efficiency of the unit, and brings significant economic benefits to the power company. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 An operation flow chart of a method for optimizing the PID parameters of a hydropower unit speed regulator applied to the power secondary frequency regulation market; Figure 2 PID parameter optimization model construction and implementation process operation flow chart. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example

[0019] This embodiment describes the process of a method for optimizing PID parameters of a hydropower unit speed regulator applied to the electric power secondary frequency regulation market.

[0020] Identify and determine the storage location of the unit's historical operating data, such as a database, log file, or remote server, confirm data access rights, ensure that the required data can be obtained legally and safely, and select key data fields for collection based on analysis requirements, including but not limited to recording the issuance time, target value, and actual execution value of each AGC instruction, unit output power, actual output power of the unit at different time points, real-time data of unit speed, and head change data during unit operation. Use SQL queries, data crawling tools, or API interfaces to extract required data from data sources to ensure the efficiency and accuracy of the data extraction process and avoid data loss or duplication; Check whether there are missing values ​​in the data set. For records with fewer missing values, consider using interpolation to fill them. If there are many missing values ​​and they have a greater impact on the analysis results, you may need to delete these records or use other methods to fill them. Use statistical methods or machine learning algorithms to detect outliers in the data. For detected outliers, modify or retain them according to the specific situation. Check whether there are duplicate records in the data set and merge or delete the duplicate records. Convert data of different dimensions to the same scale for subsequent analysis. Common normalization methods include minimum-maximum normalization and Z-score standardization. For features with large differences in numerical ranges, feature scaling is performed to reduce the impact of numerical differences on model training. Common scaling methods include linear scaling and logarithmic scaling. According to analysis requirements, the data is appropriately transformed to improve the distribution characteristics of the data. The cleaned and standardized data set is divided into training set, validation set and test set for model training and performance evaluation. After completing data cleaning and standardization, data quality checks are performed to ensure the integrity, accuracy and consistency of the data, and to check whether the data meets the requirements of subsequent analysis or model training.

[0021] Understand the role of the three links of proportion (P), integration (I) and differentiation (D) in the PID controller, adjust the output proportionally according to the deviation to quickly reduce the deviation, integrate the deviation to eliminate the steady-state error, adjust the output according to the rate of change of the deviation to suppress the further expansion of the deviation, and make it clear that the goal of the AGC system is to achieve stable signal output through gain compensation; According to the dynamic characteristics and control objectives of the AGC system, a mathematical model of the system is established, including but not limited to the input, output, and state equation of the system, and a mathematical model of the PID controller is defined. The output of the PID controller is the control quantity u, which is expressed as: ,in, The output control quantity of the controller at time t is, Proportional coefficient, deviation signal, Integration coefficient, The integral of the deviation signal from 0 to time t, Differential coefficient, is the rate of change of the error. The differential term is used to predict the trend of the error change, so as to take measures in advance. According to the objectives and constraints, the optimization objective function is defined. A comprehensive performance index function is defined, which is the weighted sum or weighted product of the response time, overshoot, number of oscillations and AGC pass rate. The initial parameters of the algorithm are set, and the optimization algorithm is selected. According to the pre-defined evaluation criteria, the optimal solution is searched in the parameter space. The first step, the specific content of the initial parameters of the particle swarm is: Number of particles : Determined by the problem size and computing resources; Inertia Weight : Usually between 0.4 and 0.9, the initial value can be set to , and gradually decreases with iterations; Learning Factor and : Generally between 1 and 2; Particle velocity range and : Determined according to the range of parameter space; Step 2: Initial parameters of the ant colony: Number of ants Can be the same as or different from the number of particles; Pheromone volatility coefficient : Usually between 0.1 and 0.5; Initial value of pheromone intensity Determined by the scale of the problem; Step 3: Searching for the optimal solution: Initialize particles and ants: Random generation The positions and velocities of particles, each particle represents a set of parameter vectors , randomly placed in parameter space Only ants; Calculate fitness: For each particle and ant, calculate the evaluation function As fitness; Particle swarm update: For each particle, update its velocity and position: ,in, and Particles In the The speed and position of the iteration, It is a particle The best location in history, is the best historical position of the entire particle swarm, and is a random number in the interval [0, 1]; Ant colony update: Ants choose the next parameter dimension to move based on pheromone concentration; Update pheromone concentration: ,in is the parameter dimension and The pheromone concentration between is the pheromone increment left by the ants on the path in this iteration; repeat the fitness calculation-particle swarm update-ant colony update until the stop condition is met; Initialize the parameter space within the range of PID parameters, use the optimization algorithm to perform iterative search in the parameter space, calculate the system performance under the current PID parameter combination in each iteration, evaluate the performance of the current PID parameter combination according to the optimization objective function, and update the optimal solution if the performance of the current PID parameter combination is better than the previous optimal solution, check whether the termination condition is met, and if the termination condition is met, stop the iteration and output the optimal PID parameter combination; Apply the optimal PID parameter combination to the actual AGC system, conduct performance tests, collect response time, overshoot, oscillation times and AGC pass rate performance index data, compare the performance index data before and after optimization, analyze the optimization effect, if the optimization effect is not ideal, adjust the optimization objective function or optimization algorithm parameters, re-optimize, and further adjust the PID parameters according to the test results to achieve better control effect, consider using integral saturation limit, differential first and filtering strategies to optimize the performance of the PID controller; Design a system architecture including PID parameter optimization model, hydropower unit speed governor control system, AGC instruction receiving module and unit status monitoring module, ensure the consistency of data communication interface and protocol between modules, so as to realize efficient data transmission and sharing, embed PID parameter optimization model into hydropower unit speed governor control system as an independent calculation module, ensure that the model can receive data from AGC instruction receiving module and unit status monitoring module in real time, design AGC instruction receiving module for receiving AGC instructions from power grid dispatching center in real time, parse and verify received AGC instructions to ensure data accuracy and completeness, design unit status monitoring module for real-time monitoring of hydropower unit operation status, collect key operation parameters of the unit, and perform preprocessing and filtering to reduce noise interference; Input the AGC instructions and unit status data received in real time into the PID parameter optimization model to ensure the real-time and accuracy of the data so that the model can make correct calculations and predictions. The PID parameter optimization model performs calculations based on the input real-time data. The model will automatically evaluate the difference between the current unit status and the AGC instructions, and calculate the optimal PID parameter combination. The calculated optimal PID parameters are output to the hydropower unit speed governor control system. The control system controls the speed of the unit according to the new PID parameters to achieve a fast and accurate response to the AGC instructions. Test the system in the actual operating environment to verify the effect of PID parameter online optimization, monitor the unit's response speed, overshoot, number of oscillations and AGC pass rate performance indicators to evaluate the system performance, adjust and optimize the PID parameter optimization model according to the test results, consider introducing more advanced optimization algorithms or adjusting the model's parameter settings to improve the system's performance and stability, iterate and improve the system according to the test results and optimization suggestions, continuously optimize the PID parameter optimization model and control strategy to adapt to different operating conditions and control requirements, monitor the system in real time to ensure the normal operation of each module and smooth data communication, promptly detect and handle system faults or abnormal conditions to ensure the reliability and stability of the system, regularly back up system data and configuration files to prevent data loss or damage, design a data recovery mechanism so that the system can be quickly restored to normal operation when data is lost or damaged, regularly maintain and upgrade the system to fix potential vulnerabilities and improve system performance, track the latest technological developments and research results, and apply new technologies and methods to the system to improve the system's advancement and competitiveness.

[0022] Plan the overall architecture of the performance monitoring system, including data acquisition module, data storage module, data analysis module and feedback control module, to ensure smooth data flow between modules, to achieve real-time data acquisition, storage and analysis, and to embed the data acquisition module in the hydropower unit speed governor control system to collect real-time operation data of the unit after adopting optimized PID parameters. The data should include but not be limited to AGC instructions, unit response speed, power change, speed fluctuation key indicators. Design an efficient data storage solution to store the collected data in a reliable database to ensure data security and integrity for subsequent data analysis and query. Develop a data analysis module to mine and analyze the stored data, and evaluate the AGC response of the unit after the optimized PID parameters are adopted by calculating performance indicators. An evaluation cycle is set to evaluate the performance of the unit regularly. Based on the evaluation results, it is determined whether the unit performance meets the preset standards or whether there is a trend of performance degradation. If the evaluation results show that the unit performance has degraded or has not met the expected standards, a new round of parameter optimization process is automatically triggered, and the PID parameter optimization model is notified to recalculate the optimal PID parameters and output them to the hydropower unit speed governor control system. The speed governor control system controls the speed of the unit according to the new PID parameters, monitors and records the operation of the unit under the new parameters for subsequent evaluation and analysis, and inputs the optimized unit performance data into the performance monitoring system again to form a closed-loop feedback control, continuously iterate the optimization process, and continuously improve the AGC response performance and stability of the unit.

[0023] Select frequency regulation market revenue, unit operating efficiency and operation and maintenance costs as the main economic indicators for comprehensive benefit evaluation, refine the calculation method and data source of each indicator to ensure the accuracy and objectivity of the evaluation, collect relevant operation data and economic indicator data of the unit before and after the optimized PID parameters, organize and analyze the data to ensure the integrity and consistency of the data, calculate the frequency regulation market revenue, operation efficiency and operation and maintenance costs of the unit before and after optimization based on the selected evaluation indicators and collected data, compare the data before and after optimization, evaluate the comprehensive benefits brought by PID parameter optimization, conduct in-depth analysis of the calculated comprehensive benefits, identify the main sources and potential problems of benefit improvement, evaluate the effectiveness and sustainability of the optimization strategy, and provide a basis for subsequent optimization iterations; According to the benefit evaluation results, analyze the advantages and disadvantages of the current optimization strategy, adjust the optimization strategy in combination with the unit operating characteristics and market demand, propose a new PID parameter optimization scheme, apply the new PID parameter optimization scheme to the unit speed governor control system, monitor and record the unit's operation under the new parameters to ensure the effective implementation of the optimization scheme, and after the optimization scheme is implemented, continuously monitor the unit's operating data and economic indicators, regularly conduct comprehensive benefit evaluation of the unit's performance to ensure the sustainability and stability of the optimization effect, and continuously adjust and optimize the PID parameter optimization strategy based on the results of continuous monitoring and evaluation to form a closed-loop iterative optimization mechanism to continuously improve unit performance and market competitiveness. Example

[0024] This embodiment describes a large hydropower station with an installed capacity of up to 500 megawatts. It is an indispensable frequency regulation force in the regional power grid. For a long time, the hydropower station has actively participated in the power secondary frequency regulation market and contributed to the stable operation of the power grid. Due to historical problems, the PID parameters of its hydropower units have been using the old configuration and have failed to keep up with the current power market demand and the changes in the unit operating status for optimization and adjustment.

[0025] We carefully collected a series of key data, including AGC instructions, unit output power, speed, and head changes, from the hydropower station’s huge historical operation database. We strictly cleaned and standardized these data, eliminated missing values ​​and outliers, and performed deduplication operations to ensure data accuracy and consistency, laying a solid foundation for subsequent modeling and analysis.

[0026] Closely combined with the actual situation of the hydropower station, a PID parameter optimization model is constructed. ,in, The output control quantity of the controller at time t is, Proportional coefficient, deviation signal, Integration coefficient, The integral of the deviation signal from 0 to time t, Differential coefficient, is the rate of change of the error. The differential term is used to predict the trend of the error change, so as to take measures in advance. According to the objectives and constraints, the optimization objective function is defined, and a comprehensive performance index function is defined. This function is the weighted sum or weighted product of the response time, overshoot, number of oscillations and AGC pass rate. It aims to find the most suitable PID parameter combination for the current unit operation state through precise mathematical description and simulation. The optimization strategy combining the particle swarm algorithm and the ant colony algorithm is carefully selected, and the advantages of the two algorithms in global search and local optimization are fully utilized. Through countless iterative calculations, the optimal solution is continuously approached. The specific content of the first step, the initial parameters of the particle swarm are as follows: Number of particles : Determined by the problem size and computing resources; Inertia Weight : Usually between 0.4 and 0.9, the initial value can be set to , and gradually decreases with iterations; Learning Factor and : Generally between 1 and 2; Particle velocity range and : Determined according to the range of parameter space; Step 2: Initial parameters of the ant colony: Number of ants Can be the same as or different from the number of particles; Pheromone volatility coefficient : Usually between 0.1 and 0.5; Initial value of pheromone intensity Determined by the scale of the problem; Step 3: Searching for the optimal solution: Initialize particles and ants: Random generation The positions and velocities of particles, each particle represents a set of parameter vectors , randomly placed in parameter space Only ants; Calculate fitness: For each particle and ant, calculate the evaluation function As fitness; Particle swarm update: For each particle, update its velocity and position: ,in, and Particles In the The speed and position of the iteration, It is a particle The best location in history, is the best historical position of the entire particle swarm, and is a random number in the interval [0, 1]; Ant colony update: Ants choose the next parameter dimension to move based on pheromone concentration; Update pheromone concentration: ,in is the parameter dimension and The pheromone concentration between It is the pheromone increment left by the ants on the path in this iteration; the fitness-particle swarm update-ant colony update are calculated repeatedly until the stop condition is met, and the carefully optimized PID parameter model is seamlessly integrated into the speed regulator control system of the hydropower station to realize the online dynamic adjustment function of the PID parameters. When the hydropower station receives the AGC instruction from the power grid, the system can respond quickly, automatically calculate and output the current optimal PID parameter combination, ensuring that the unit can respond to the instruction at the fastest speed and highest accuracy, and realize precise regulation of power output.

[0027] Build a complete performance monitoring system to track and record the AGC response of the unit after optimizing PID parameters in real time. Through regular performance evaluation, timely detect signs of unit performance degradation and automatically trigger a new round of parameter optimization process. This closed-loop feedback mechanism can ensure that the unit always maintains the best operating state and provide strong guarantee for the stable operation of the power grid.

[0028] Combining the multi-dimensional economic indicators of frequency regulation market revenue, unit operating efficiency and operation and maintenance cost, a comprehensive evaluation is conducted on the comprehensive benefits brought by PID parameter optimization. Through in-depth analysis of the data comparison before and after optimization, the effectiveness and sustainability of the optimization strategy are objectively evaluated. According to the evaluation results, the optimization strategy is continuously adjusted, and new optimization plans are proposed and implemented. At the same time, the changes in operating data and economic indicators are continuously monitored, the benefits are regularly evaluated and the PID parameter optimization strategy is adjusted to form a closed-loop iterative optimization mechanism. Through this continuous optimization and iteration process, the unit performance and market competitiveness are continuously improved, creating greater economic and social benefits for the hydropower station.

[0029] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for optimizing PID parameters of a hydropower unit speed regulator applied to the power secondary frequency regulation market, characterized in that: The specific steps of this method are: S100, data collection and preprocessing: collect historical operation data of the unit, including but not limited to AGC instructions, unit output power, speed, head change, and clean and standardize the data; S200, establishing a PID parameter optimization model: according to the dynamic characteristics and control objectives of the AGC system, a mathematical model of the system is established, including but not limited to the input, output, and state equation of the system, and a mathematical model of the PID controller is defined. The output of the PID controller is the control quantity u, which is expressed as: ,in, represents the output control quantity of the controller at time t, represents the proportionality coefficient, Indicates the deviation signal, represents the integral coefficient, represents the integral of the deviation signal from 0 to time t, represents the differential coefficient, It represents the rate of change of the error. The differential term is used to predict the trend of the error change, so as to take measures in advance. According to the objectives and constraints, the optimization objective function is defined, the initial parameters of the algorithm are set, the optimization algorithm is selected, and the optimal solution is searched in the parameter space according to the pre-defined evaluation criteria. Initialize the parameter space within the range of PID parameters, use the optimization algorithm to perform iterative search in the parameter space, calculate the system performance under the current PID parameter combination in each iteration, evaluate the performance of the current PID parameter combination according to the optimization objective function, and update the optimal solution if the performance of the current PID parameter combination is better than the previous optimal solution, check whether the termination condition is met, and if the termination condition is met, stop the iteration and output the optimal PID parameter combination; Apply the optimal PID parameter combination to the actual AGC system, conduct performance tests, collect response time, overshoot, oscillation times and AGC pass rate performance index data, compare the performance index data before and after optimization, analyze the optimization effect, if the optimization effect is not ideal, adjust the optimization objective function or optimization algorithm parameters, re-optimize, further adjust the PID parameters according to the test results, consider using integral saturation limit, differential first and filtering strategies to optimize the performance of the PID controller; S300, online parameter optimization and adjustment: Integrate the above model into the hydropower unit speed governor control system to achieve online dynamic adjustment of PID parameters. According to the real-time received AGC instructions and unit operating status, the model automatically calculates and outputs the optimal PID parameters, so that the unit can respond to AGC instructions quickly and accurately. S400, Performance Monitoring and Feedback: Establish a performance monitoring system to continuously track and record the AGC response of the unit after the optimized PID parameters are adopted. If performance degradation is found through regular evaluation, a new round of parameter optimization process will be automatically triggered; S500, benefit evaluation and optimization iteration: Combined with the frequency regulation market revenue, unit operating efficiency and operation and maintenance cost economic indicators, the comprehensive benefits brought by PID parameter optimization are evaluated. According to the evaluation results, the optimization strategy is continuously adjusted to form a closed-loop iterative optimization mechanism to continuously improve the unit performance and market competitiveness.

2. According to claim 1, a method for optimizing PID parameters of a hydropower unit speed regulator applied to the electric power secondary frequency modulation market is characterized in that: The S100 data collection involves determining the location and access rights of the data source, selecting key fields including but not limited to AGC instructions, unit output power, speed and head changes, and extracting data using SQL queries, data crawling tools or API interfaces. Data cleaning includes processing missing values, detecting and processing outliers, and deduplication. Data standardization processing involves normalization, feature scaling, data conversion, and dividing the data set into training sets, validation sets, and test sets.

3. According to claim 1, a method for optimizing PID parameters of a hydropower unit speed regulator applied to the electric power secondary frequency regulation market is characterized in that: The S200 establishes the initial parameters of the PID parameter optimization model setting algorithm, selects the optimization algorithm, and searches for the optimal solution in the parameter space according to the predefined evaluation criteria, which specifically includes three steps: the first step is the initial parameters of the particle swarm part, the second step is the initial parameters of the ant colony part, and the third step is the optimal solution search process; The first step, the specific contents of the initial parameters of the particle swarm are: Number of particles : Determined by the problem size and computing resources; Inertia Weight : Usually between 0.4 and 0.9, the initial value can be set to , and gradually decreases with iterations; Learning Factor and : Generally between 1 and 2; Particle velocity range and : Determined according to the range of parameter space; Step 2: Initial parameters of the ant colony: Number of ants Can be the same as or different from the number of particles; Pheromone volatility coefficient : Usually between 0.1 and 0.5; Initial value of pheromone intensity Determined by the scale of the problem; Step 3: Searching for the optimal solution: Initialize particles and ants: Random generation The positions and velocities of particles, each particle represents a set of parameter vectors , randomly placed in parameter space Only ants; Calculate fitness: For each particle and ant, calculate the evaluation function As fitness; Particle swarm update: For each particle, update its velocity and position: ,in, and Particles In the The speed and position of the iteration, It is a particle The best location in history, is the best historical position of the entire particle swarm, and is a random number in the interval [0, 1]; Ant colony update: Ants choose the next parameter dimension to move based on pheromone concentration; Update pheromone concentration: ,in is the parameter dimension and The pheromone concentration between is the pheromone increment left by the ants on the path in this iteration; repeat the fitness calculation-particle swarm update-ant colony update until the stopping condition is met.

4. According to claim 1, a method for optimizing PID parameters of a hydropower unit speed regulator applied to the electric power secondary frequency regulation market is characterized in that: The S300 online parameter optimization and adjustment system architecture design covers PID parameter optimization model, hydropower unit speed governor control system, AGC command receiving and unit status monitoring module. A unified data communication interface and protocol are used between the modules. The PID parameter optimization model is embedded in the speed governor control system as an independent calculation module to receive AGC commands and unit status data in real time.

5. According to claim 4, a method for optimizing PID parameters of a hydropower unit speed regulator applied to the electric power secondary frequency regulation market is characterized in that: The S300 online parameter optimization and adjustment includes data reception and processing, PID parameter online optimization, testing and optimization, and operation and maintenance processes. The data receiving module parses the AGC instructions in real time and monitors the unit status, preprocesses key parameters, and the PID parameter optimization model calculates the optimal parameters based on real-time data and outputs them to the speed governor control system. After the system is actually tested, it is iteratively optimized based on the results. During operation, real-time monitoring, data backup and recovery, and regular maintenance and upgrades are implemented.

6. According to claim 1, a method for optimizing PID parameters of a hydropower unit speed regulator applied to the electric power secondary frequency regulation market is characterized in that: The S400 performance monitoring and feedback establishes a performance monitoring system, including data acquisition, data storage, data analysis modules and feedback control modules.

7. According to claim 1, a method for optimizing PID parameters of a hydropower unit speed regulator applied to the electric power secondary frequency regulation market, characterized in that: The S400 performance monitoring and feedback regularly evaluates the unit performance. If the performance does not meet the standard or declines, the parameter optimization process is automatically triggered, and the PID parameter optimization model recalculates and outputs new parameters to the speed governor control system. After the optimization is implemented, the unit operation status is monitored and recorded, and the data is fed back to the system to form a closed-loop iterative optimization.

8. According to claim 1, a method for optimizing PID parameters of a hydropower unit speed regulator applied to the electric power secondary frequency regulation market is characterized in that: In the benefit evaluation phase of the S500 benefit evaluation and optimization iteration, frequency regulation market revenue, unit operating efficiency and operation and maintenance cost are selected as evaluation indicators, the calculation method is refined and data before and after PID parameter optimization is collected, benefit calculation and in-depth analysis are performed, and the effectiveness and sustainability of the optimization strategy are evaluated. In the optimization iteration phase, the optimization strategy is adjusted according to the evaluation results, a new solution is proposed and applied to the unit speed governor control system, the operating data and economic indicators are continuously monitored, the benefits are regularly evaluated, and the PID parameter optimization strategy is continuously adjusted to form a closed-loop iterative mechanism.

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