Method, system, equipment and medium for optimizing PID parameters in automatic power generation control of hydropower station by using historical genetic algorithm

Abstract

The invention discloses a method, system, equipment and medium for optimizing PID parameters in hydropower station automatic power generation control by using a historical genetic algorithm, and relates to the technical field of power system automatic control, and the method comprises the steps: collecting historical operation data of a hydropower station automatic power generation control system, and carrying out the initialization; designing an improved fitness function based on the historical data, and correcting the fitness function; pID parameters are optimized through a genetic algorithm, and an optimal solution meeting the requirement of the hydropower station is generated; the optimized PID parameters are applied to an automatic power generation control system of the hydropower station, and optimal control over power and frequency is achieved; historical data are continuously updated, and PID parameters keep optimal performance in a dynamic environment. According to the method, the population quality of the genetic algorithm is improved by utilizing historical data, the convergence speed is increased, adaptive optimization of PID parameters is realized, the response speed and stability of the system under the condition of load fluctuation are improved, the anti-interference capability is improved, and the stability of frequency and power is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power system automation control, and particularly to a method, system, device and medium for optimizing PID parameters in automatic generation control of a hydropower station by using a historical genetic algorithm. Background Technique

[0002] Automatic Generation Control (AGC) is an important real-time regulation means, and its function is to maintain the stability of system frequency and power when the load changes. The PID controller is widely used in the AGC system because of its simple operation and good regulation effect. However, the parameter adjustment and optimization of the PID controller are a key problem. The traditional manual parameter adjustment method is difficult to meet the complex dynamic characteristics, with low efficiency and low adjustment accuracy.

[0003] In recent years, intelligent optimization algorithms such as genetic algorithms and particle swarm optimization have been introduced into the optimization of Proportional-Integral-Derivative Control (PID) parameters. Although certain effects have been achieved, there are still problems such as large computational amount, slow convergence speed, and easy to fall into local optimum. In view of these deficiencies, the present invention proposes a PID parameter optimization method based on the Historical Genetic Algorithm (HGA), which uses historical operation data to improve the optimization efficiency and control effect. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is: how to solve the problems that although certain effects have been achieved when intelligent optimization algorithms such as genetic algorithms and particle swarm optimization are introduced into the optimization of PID parameters, there are still problems such as large computational amount, slow convergence speed, and easy to fall into local optimum.

[0006] To solve the above technical problems, the present invention provides the following technical solution: A method for optimizing PID parameters in the automatic generation control of a hydropower station using a historical genetic algorithm, including collecting historical operation data of the automatic generation control system of the hydropower station and initializing it; designing an improved fitness function based on the historical data and correcting the fitness function; the design of the improved fitness function includes obtaining the fitness function by combining real-time performance and historical optimization effects, and designing an improved fitness function by introducing historical data; using the genetic algorithm to optimize the PID parameters to generate an optimal solution that meets the requirements of the hydropower station; the use of the genetic algorithm to optimize the PID parameters includes optimizing the PID parameters through the selection, crossover, and mutation operations of the genetic algorithm; applying the optimized PID parameters to the automatic generation control system of the hydropower station to achieve optimized control of power and frequency; continuously updating the historical data so that the PID parameters maintain the best performance in a dynamic environment.

[0007] As a preferred embodiment of the method for optimizing PID parameters in the automatic generation control of a hydropower station using a historical genetic algorithm according to the present invention, wherein: the historical operation data includes frequency deviation, power output, and load fluctuation. Collecting the historical operation data of the automatic generation control system of the hydropower station to form a historical data set, which is expressed as: , wherein, is the frequency deviation of the data point at the th sampling moment, is the power output of the data point at the th sampling moment, is the load fluctuation of the data point at the th sampling moment, is the number of historical data samples, is the data point at the sampling moment.

[0008] As a preferred embodiment of the method for optimizing PID parameters in the automatic generation control of a hydropower station using a historical genetic algorithm according to the present invention, wherein: the initialization includes initializing the genetic algorithm population according to the historical data, which is expressed as: , wherein, is the population size, that is, the number of individuals included in each generation of the population, are the proportional, integral, and differential parameters of the PID controller respectively.

[0009] As a preferred embodiment of the method for optimizing PID parameters in the automatic generation control of a hydropower station using a historical genetic algorithm according to the present invention, wherein: the designed improved fitness function is expressed as: , wherein, is the fitness, is the integral square error, which measures the cumulative value of the squared frequency deviation, is the integral time-weighted absolute error, which reflects the time influence of the dynamic error, is the response time deviation, which measures the dynamic response speed of the system, is the weight coefficient of the integral square error, is the weight coefficient of the integral time-weighted absolute error, is the weight coefficient of the response time deviation; The integral square error is expressed as: , where, is the value of the system frequency deviation from the target frequency at a certain moment , is the time; The integral time-weighted absolute error is expressed as: , The response time deviation is expressed as: , where, is the system settling time.

[0010] As a preferred scheme of the method for optimizing PID parameters in the automatic generation control of a hydropower station by using a historical genetic algorithm according to the present invention, wherein: The modification of the fitness function includes adjusting the fitness function by using historical data, which is expressed as: , where, is the adjusted fitness, is the balance factor, is the fitness calculated from historical data; , where, is the integral square error of the historical frequency deviation, is the historical integral time-weighted absolute error, historical response time deviation.

[0011] As a preferred scheme of the method for optimizing PID parameters in the automatic generation control of a hydropower station by using a historical genetic algorithm according to the present invention, wherein: The selection includes using roulette wheel selection, and the probability of an individual being selected is determined according to the fitness value , which is expressed as: , where, is the The power output values of a population; the crossover includes generating new individuals using two-point crossover, expressed as: , , , wherein, are respectively the proportional, integral, and derivative parameters of the PID controller after crossover update, , are the positions of two crossover points, is the adjustment factor; the mutation includes introducing random perturbations to the parameters of the selected individuals, expressed as: , , , wherein, is the mutation rate, , , are respectively the random perturbations of the proportion, integral, and derivative, , , are respectively the values of the proportional, integral, and derivative parameters after the mutation operation.

[0012] As a preferred scheme of the method for optimizing PID parameters in hydropower station automatic generation control using the historical genetic algorithm according to the present invention, wherein: applying the optimized PID parameters to the hydropower station automatic generation control system includes selecting the individual with the highest fitness in the evolved population as the optimal PID parameter group, expressed as:

[0013] During the operation process, continuously introduce new operation data to update the historical data set D, and adjust the fitness function and the population to ensure the adaptive optimization ability of the PID parameters.

[0014] The present invention provides a system for the method of optimizing PID parameters in hydropower station automatic generation control using the historical genetic algorithm, which can solve the problem of optimizing PID parameters in hydropower station automatic generation control using the historical genetic algorithm by constructing a system for the method of optimizing PID parameters in hydropower station automatic generation control using the historical genetic algorithm.

[0015] To solve the above technical problems, the present invention provides the following technical solutions: A system for optimizing PID parameters in the automatic generation control of a hydropower station using a historical genetic algorithm, including a data acquisition module, a design correction module, a PID parameter optimization module, a control module, and an update module; the data acquisition module is used to collect historical operation data of the automatic generation control system of the hydropower station and perform initialization; the design correction module is used to design an improved fitness function based on historical data and correct the fitness function; the PID parameter optimization module is used to optimize the PID parameters using a genetic algorithm to generate an optimal solution that meets the requirements of the hydropower station; the control module is used to apply the optimized PID parameters to the automatic generation control system of the hydropower station to achieve optimized control of power and frequency; the update module is used to continuously update historical data so that the PID parameters maintain the best performance in a dynamic environment.

[0016] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method for optimizing PID parameters in the automatic generation control of a hydropower station using a historical genetic algorithm as described above.

[0017] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the method for optimizing PID parameters in the automatic generation control of a hydropower station using a historical genetic algorithm as described above.

[0018] The beneficial effects of the present invention are as follows: The method for optimizing PID parameters in the automatic generation control of a hydropower station using a historical genetic algorithm provided by the present invention improves the optimization efficiency, uses historical data to improve the quality of the genetic algorithm population, and speeds up the convergence speed. It enhances the dynamic response performance, realizes the adaptive optimization of PID parameters, and improves the response speed and stability of the system under load fluctuations. It improves the robustness of the control system, enhances the anti-interference ability of the AGC system in a complex environment, and ensures the stability of frequency and power. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0020] Figure 1 It is a flowchart of a method for optimizing PID parameters in the automatic generation control of a hydropower station using a historical genetic algorithm provided by the first embodiment of the present invention.

[0021] Figure 2Structural diagram of a system for optimizing PID parameters in automatic generation control of a hydropower station using a historical genetic algorithm provided for the second embodiment of the present invention.

[0022] In the figure, 100 is a data acquisition module; 200 is a design correction module; 300 is a PID parameter optimization module; 400 is a control module; 500 is an update module. Detailed implementation manners

[0023] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific implementation manners of the present invention will be given in conjunction with the accompanying drawings of the specification.

[0024] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0025] Example 1, referring to Figure 1 , which is the first embodiment of the present invention. This embodiment provides a method for optimizing PID parameters in automatic generation control of a hydropower station using a historical genetic algorithm, including: collecting historical operation data of the automatic generation control system of the hydropower station and initializing it; designing an improved fitness function based on the historical data and correcting the fitness function; optimizing the PID parameters using the genetic algorithm to generate an optimal solution suitable for the requirements of the hydropower station; applying the optimized PID parameters to the automatic generation control system of the hydropower station to achieve optimized control of power and frequency; continuously updating the historical data so that the PID parameters maintain the best performance in a dynamic environment.

[0026] Solve the problems of low optimization efficiency and low accuracy of PID parameters in the existing technology. By introducing historical operation data into the optimization process, the convergence speed and optimization effect of the algorithm are improved, and the dynamic response ability and steady-state performance of the AGC system are enhanced.

[0027] S1. Collect historical operation data of the automatic generation control system of the hydropower station and initialize it.

[0028] Collect historical operation data of the hydropower station AGC system, including frequency deviation, power output, and load fluctuation, etc., to form a historical data set: , Wherein, is the frequency deviation of the data point at the th sampling moment, is the power output of the data point at the th sampling moment, is the The load fluctuation of the data points at a sampling moment, is the number of historical data samples, is the data point at the sampling moment, and its value range is strictly limited to 1 ≤ ≤ , ensuring that each index corresponds to valid data without out-of-bounds problems. The historical data of the hydropower station AGC system is usually recorded at a fixed sampling interval (such as second level or minute level), forming a discrete time series. As the discrete time point index, it is directly related to the sampling order, and the time series relationship can be characterized without explicitly recording the absolute timestamp. For example: = 1 represents the first sampling moment, = represents the last sampling moment, and the time interval between adjacent indices and +1 is determined by the sampling frequency.

[0029] Initialize the genetic algorithm population according to historical data: , wherein, is the population size, that is, the number of individuals included in each generation of the population, are the proportional, integral, and derivative parameters of the PID controller respectively.

[0030] S2. Design an improved fitness function based on historical data and correct the fitness function.

[0031] Design an improved fitness function by introducing historical data.

[0032] Combining real-time performance and historical optimization effect, the fitness function is obtained as: , wherein, is the fitness, is the integral square error, measuring the cumulative value of the square of the frequency deviation, is the integral time weighted absolute error, reflecting the time influence of the dynamic error, is the response time deviation, measuring the dynamic response speed of the system, is the weight coefficient of the integral square error, is the weight coefficient of the integral time weighted absolute error, is the weight coefficient of the response time deviation.

[0033] The integral square error is expressed as: , wherein, is at a certain moment The value of the system frequency deviation from the target frequency is time.

[0034] The integral time weighted absolute error is expressed as: , The response time deviation is expressed as: , wherein, in the control system and the optimization algorithm, represents the system stabilization time or the end time of the transient process, that is, the time when the output signal of the system finally reaches and remains within the set target range after being disturbed or adjusted. This time is used to measure the time required for the system to stabilize from the initial state to the steady state.

[0035] One of the main objectives of AGC is to maintain the stability of the system frequency. The frequency deviation directly reflects the state of the system power balance, so it may be the core index for control optimization. The power output and load fluctuations may be used as inputs or constraints, rather than directly as optimization objectives. In addition, the PID controller itself may mainly respond to the frequency deviation.

[0036] Adjust the fitness function using historical data: , wherein, is the adjusted fitness, is the balance factor, is the fitness calculated from historical data.

[0037] , wherein, is the integral square error of the historical frequency deviation, is the historical integral time weighted absolute error, historical response time deviation.

[0038] S3. Optimize the PID parameters using the genetic algorithm to generate the optimal solution suitable for the hydropower station requirements.

[0039] Population evolution, optimize the PID parameters through the selection, crossover, and mutation operations of the genetic algorithm: Selection: Use roulette wheel selection, and determine the individual selection probability according to the fitness value : , wherein, is the power output value of the

[0040] Crossover includes using two-point crossover to generate new individuals, expressed as: , , , Among them, are the proportional, integral, and derivative parameters of the PID controller after cross-update respectively, is the adjustment factor, , refer to two crossover points. For example, in genetic algorithms, two-point crossover will randomly select two gene positions ( and ), and exchange the gene segments between these two positions.

[0041] Mutation includes introducing random perturbations to the parameters of the selected individuals, expressed as: , , , Among them, is the mutation rate, , , are the random perturbations of the proportional, integral, and derivative respectively, , , are the proportional, integral, and derivative parameter values after mutation operation respectively.

[0042] In the hydropower station AGC control based on the historical genetic algorithm, represents the proportional parameter value after mutation operation. This symbol comes from the mutation step of the genetic algorithm. This symbol appears in the mutation operation link of the population evolution stage, and is used to represent the new value of the PID proportional parameter after introducing random perturbations, which belongs to the core step of optimizing PID parameters by genetic algorithms.

[0043] The initial population evolves through operations such as selection (roulette wheel), crossover (two-point crossover), and mutation (introducing random perturbations). For example, the crossover operation may generate new individuals from parameter combinations with excellent historical performance, accelerating convergence to the global optimal solution.

[0044] S4. Apply the optimized PID parameters to the hydropower station automatic generation control system to achieve optimized control of power and frequency.

[0045] Select the individual with the highest fitness in the evolved population as the optimal PID parameter group: , S5. Continuously update the historical data so that the PID parameters maintain the best performance in the dynamic environment.

[0046] During the operation process, continuously introduce new operation data to update the historical dataset D, and adjust the fitness function and the population to ensure the self-adaptive optimization ability of the PID parameters.

[0047] A method for optimizing the PID controller parameters based on the Historical Genetic Algorithm (HGA) is realized.

[0048] Data collection and initialization. In this step, first collect the historical operation data of the hydropower station AGC system (frequency deviation, power output, load fluctuation, etc.). These historical data provide the necessary information for the subsequent optimization process. The historical data provides the basis for the initialization of the genetic algorithm population. The characteristics of the historical data provide "prior knowledge" for the optimization process, enabling the genetic algorithm to start from a population closer to the global optimal solution and improving the optimization efficiency.

[0049] Design of the Historical Genetic Algorithm. In this step, an improved fitness function is designed, and the structure of the fitness function is adjusted by introducing historical data. The purpose of the fitness function is to measure the performance of the PID controller during the system operation, combining historical data with real-time performance. This improved fitness function not only considers the current performance of the PID controller (such as frequency deviation, response time, etc.), but also combines the performance in historical data for correction. The introduction of historical data improves the convergence speed and optimization accuracy of the algorithm, avoiding the limitations of simply relying on the current state.

[0050] Historical data correction. By introducing historical data to correct the fitness function, the optimization of the PID controller not only considers the current dynamic performance but also takes into account the historical adjustment effect. The goal of historical data correction is to enhance the self-adaptability of the controller parameters, enabling the system to perform more efficient optimization based on past data. Historical data correction is the key based on the design of the Historical Genetic Algorithm, directly affecting the evaluation criteria and optimization effect of the fitness function. It is closely connected with the design step of the Historical Genetic Algorithm. By adjusting the weight of the fitness function through historical data, the genetic algorithm can use more comprehensive information for searching.

[0051] Population evolution. Optimize the PID parameters through the basic operations of the genetic algorithm (selection, crossover, mutation). The evolution process of the population selects superior individuals through fitness values and generates new individuals through crossover and mutation. After multiple iterations, the most suitable PID controller parameters are finally optimized. This part is the core of the entire optimization process, involving the specific implementation of the genetic algorithm. The fitness function (including the fitness after historical data correction) directly affects the effect of population evolution. During the evolution process, the information provided by historical data (such as historical performance) will be reflected in the population selection and parameter update process, thereby improving the optimization efficiency.

[0052] Parameter optimization and control: After the population evolution is completed, the individual with the highest fitness (i.e., the optimal PID parameter group) is selected and applied to the AGC control system to achieve the optimal control of the power and frequency of the hydropower station. This step completes the optimization task of the PID parameters and is used for the actual system control. This step is the result of all the previous steps - from data collection to population evolution, and finally the system control is carried out by selecting the optimal PID parameters. The successful implementation of the historical genetic algorithm in the optimization process ensures that the PID parameters can adapt to the dynamic requirements of the hydropower station AGC system and improves the regulation ability of the system.

[0053] Real-time update and adaptive optimization: During the operation process, as new operation data is continuously introduced, the historical data set is also updated. Through this continuous adaptive optimization, the parameters of the PID controller can be continuously adjusted according to the latest state of the power system to maintain the optimization effect. This part forms a closed loop with the previous steps. By real-time updating the historical data and adjusting the fitness function, it is ensured that the PID controller can be continuously optimized according to the latest operation state, so that the control system can adapt to the changing environment and load fluctuations. This step is closely connected with all the previous steps to ensure that the system continuously maintains the best control performance during long-term operation.

[0054] Each step of the entire invention forms a step-by-step process through the introduction and optimization of historical data: Data collection and initialization provide the basic data for subsequent optimization.

[0055] The design of the historical genetic algorithm introduces historical data to improve the optimization efficiency.

[0056] Historical data correction makes the fitness function more accurate and takes into account the historical performance, avoiding the deficiency of only relying on real-time data.

[0057] Population evolution finally generates the optimized PID controller parameters through the selection, crossover, and mutation operations of the genetic algorithm.

[0058] Parameter optimization and control is to apply the optimized PID parameters to the actual control to achieve the stable operation of the AGC system.

[0059] Real-time update and adaptive optimization ensure that the PID parameters always maintain adaptability and the best performance in the changing power system by continuously updating the historical data set.

[0060] Each step is interconnected, jointly ensuring the efficient optimization of the PID parameters, making the hydropower station AGC system maintain higher stability and robustness in load changes and dynamic responses.

[0061] Example 2, refer to Figure 2, which is the second embodiment of the present invention. Different from the previous embodiment, it provides a system for optimizing PID parameters in the automatic generation control of a hydropower station by using a historical genetic algorithm, including: a data acquisition module 100, a design correction module 200, a PID parameter optimization module 300, a control module 400, and an update module 500.

[0062] The data acquisition module 100 is used to collect the historical operation data of the automatic generation control system of the hydropower station and perform initialization.

[0063] The design correction module 200 is used to design an improved fitness function based on the historical data and correct the fitness function.

[0064] The PID parameter optimization module 300 is used to optimize the PID parameters by using a genetic algorithm and generate an optimal solution suitable for the requirements of the hydropower station.

[0065] The control module 400 is used to apply the optimized PID parameters to the automatic generation control system of the hydropower station to achieve optimized control of power and frequency.

[0066] The update module 500 is used to continuously update the historical data so that the PID parameters can maintain the best performance in a dynamic environment.

[0067] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0068] The logic and / or steps represented in the flowchart or otherwise described herein can, for example, be considered a definitional sequence of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. As used in this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0069] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0070] It should be understood that the various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0071] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for optimizing PID parameters in the automatic generation control of a hydropower station by using a historical genetic algorithm, characterized in that: including Collecting the historical operation data of the hydropower station's automatic generation control system and initializing it; Designing an improved fitness function based on the historical data and correcting the fitness function; The design of the improved fitness function includes obtaining the fitness function by combining real-time performance and historical optimization effects, and designing an improved fitness function by introducing historical data; Using the genetic algorithm to optimize the PID parameters to generate the optimal solution suitable for the hydropower station's requirements; The use of the genetic algorithm to optimize the PID parameters includes optimizing the PID parameters through the selection, crossover, and mutation operations of the genetic algorithm; Applying the optimized PID parameters to the hydropower station's automatic generation control system to achieve optimized control of power and frequency; Continuously updating the historical data so that the PID parameters maintain the best performance in a dynamic environment.

2. A method for optimizing PID parameters in the automatic generation control of a hydropower station using a historical genetic algorithm as claimed in claim 1, characterized in that: The historical operation data includes frequency deviation, power output, and load fluctuation. Collecting the historical operation data of the hydropower station's automatic generation control system to form a historical data set, denoted as ， wherein, is the frequency deviation of the data point at the -th sampling moment, is the power output of the data point at the -th sampling moment, is the load fluctuation of the data point at the -th sampling moment, is the number of historical data samples, is the data point at the sampling moment.

3. The method for optimizing PID parameters in the automatic generation control of a hydropower station by using a historical genetic algorithm according to claim 2, characterized in that: The initialization includes initializing the genetic algorithm population according to the historical data, denoted as ， Among them, is the population size, that is, the number of individuals included in each generation of the population, are the proportional, integral, and derivative parameters of the PID controller, respectively.

4. A method for optimizing PID parameters in the automatic generation control of a hydropower station using a historical genetic algorithm as claimed in claim 3, characterized in that: The designed improved fitness function is denoted as ， Among them, is the fitness,[[]]END]] is the integral square error, which measures the cumulative value of the squared frequency deviation.[[]]END]] is the integral time weighted absolute error, which reflects the time influence of the dynamic error.[[]]END]] is the response time deviation, which measures the dynamic response speed of the system.[[]]END]] is the weight coefficient of the integral square error.[[]]END]] is the weight coefficient of the integral time weighted absolute error.[[]]END]] is the weight coefficient of the response time deviation;[[]]END]] The integral square error is denoted as ， wherein, is the value of the system frequency deviating from the target frequency at a certain moment, and is time; The integral time weighted absolute error is denoted as ， The response time deviation is denoted as ， Among them, is the system stability time.

5. A method for optimizing PID parameters in the automatic generation control of a hydropower station using a historical genetic algorithm, as described in claim 4, characterized in that: The correction of the fitness function includes adjusting the fitness function using historical data, denoted as ， Among them, is the adjusted fitness,[ is the balance factor,[ is the fitness calculated from historical data.[ ， wherein, is the integrated square error of the historical frequency deviation, is the historical integrated time-weighted absolute error, historical response time deviation.

6. A method for optimizing PID parameters in the automatic generation control of a hydropower station using a historical genetic algorithm as claimed in claim 5, characterized in that: The selection includes using roulette wheel selection, and according to the fitness value to determine the selection probability of an individual, expressed as ， Among them, is the power output value of the th population; The crossover includes generating new individuals using two-point crossover, denoted as ， ， ， Among them, are respectively the proportional, integral, and derivative parameters of the PID controller after cross-update, , are the positions of two crossover points, is the adjustment factor; The mutation includes introducing random perturbations to the parameters of the selected individuals, denoted as ， ， ， Among them, is the mutation rate, , , are the random perturbations of proportional, integral, and differential respectively, , , are the proportional, integral, and differential parameter values after the mutation operation respectively.

7. A method for optimizing PID parameters in the automatic generation control of a hydropower station by using a historical genetic algorithm, characterized in that: Applying the optimized PID parameters to the hydropower station's automatic generation control system includes selecting the individual with the highest fitness in the evolved population as the optimal PID parameter group, denoted as ， During operation, continuously introducing new operation data to update the historical data set D, adjusting the fitness function and the population to ensure the self-adaptive optimization ability of the PID parameters.

8. A system for optimizing PID parameters in the automatic generation control of a hydropower station using a historical genetic algorithm, applying a method for optimizing PID parameters in the automatic generation control of a hydropower station using a historical genetic algorithm as described in any one of claims 1 to 7, characterized in that: Including a data acquisition module (100), a design correction module (200), a PID parameter optimization module (300), a control module (400), and an update module (500); The data acquisition module (100) is used to collect the historical operation data of the hydropower station's automatic generation control system and initialize it; The design correction module (200) is used to design an improved fitness function based on the historical data and correct the fitness function; The PID parameter optimization module (300) is used to optimize the PID parameters using the genetic algorithm to generate the optimal solution suitable for the hydropower station's requirements; The control module (400) is used to apply the optimized PID parameters to the hydropower station's automatic generation control system to achieve optimized control of power and frequency; The update module (500) is used to continuously update the historical data so that the PID parameters maintain the best performance in a dynamic environment.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for optimizing the PID parameters in the automatic generation control of hydropower stations using the historical genetic algorithm according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for optimizing the PID parameters in the automatic generation control of hydropower stations using the historical genetic algorithm according to any one of claims 1 to 7.

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