Hydro-generator regulation and control multi-agent modeling and state optimizing method

Abstract

According to the hydro-generator regulation and control multi-agent modeling and state optimizing method, the mathematical model of the hydro-generator regulation and control system is established based on the characteristics of the hydro-generator regulation and control device and the controlled object, and the step disturbance signal is introduced into the established model; performing model identification on a regulator part of the regulation and control system, a water turbine and a water diversion system part of the water turbine by adopting multiple agents; the method can ensure polynomial time convergence and avoid premature convergence, the improved multi-agent algorithm increases the global search ability in a solution space, breaks a local solution by means of external force, and avoids the problems of premature convergence and genetic stability, and the result proves that the algorithm is effective, and the algorithm is suitable for large-scale popularization and application. The establishment of the accurate mathematical model of the water turbine regulation and control system is of great significance to the analysis and calculation of a power system and the improvement of the control performance of the regulator, the power generation efficiency and quality of the water turbine generator set are improved, and the performance improvement plays a positive role in the quality, economic benefit and safe operation of a hydraulic power plant and the power supply thereof.

Description

Technical Field

[0001] The present application relates to a method for modeling and optimizing a state of a water turbine control, and in particular to a method for modeling and optimizing a state of a water turbine generator control multi-agent, belonging to the technical field of electric power control models. Background Art

[0002] At present, the problem of safe operation of power grid system is becoming more and more prominent, and the control performance requirements of power equipment are getting higher and higher. Modeling and parameter testing of turbine control system is one of the problems of power generation control modeling that needs to be solved urgently. The turbine control system uses turbine governor as controller, which is a closed-loop control system composed of turbine generator set and water diversion system as controlled objects. It is a nonlinear and complex system with non-minimum phase in which water, machine and electricity interact with each other. The basic task of turbine control system is to continuously control the active power of turbine generator set according to the change of load, so as to maintain the speed (frequency) of the unit within the prescribed range, so as to control the frequency of the power grid to remain stable. Turbine generator set often performs frequency regulation and peak regulation tasks in the power grid, and is frequently started and stopped. Its performance and automation level directly affect the normal operation of the unit. Therefore, the performance of turbine control system has a significant impact on the power quality and safe and reliable operation of the power system.

[0003] With the increasing development of large-scale interconnected power systems with ultra-high voltage long-distance power transmission, the power consumption departments have continuously improved their requirements for power quality. The power consumption departments have put forward higher requirements for the control quality, control stability boundary, power generation efficiency of turbines, and optimal control of hydro-mechanical-electrical combined control of turbine control systems. The model parameters of the controlled object of the turbine control system are related to the operating conditions and loads of the unit itself. The parameters of the turbine model vary greatly with different operating conditions. The changes in the operating conditions and loads of the generator will also affect some parameters of the generator model. With the expansion of the production scale of hydropower plants and the widespread application of medium and large units, the increase in the complexity of power systems and the increasing number of various control methods, the control and stability problems of power systems have become increasingly complex, which has also put forward higher and higher requirements for the control of hydropower plant units. This requirement has become not only limited to the control quality requirements of a single control system, but also must consider the coupling effects between the control processes, so as to obtain overall system stability and overall performance improvement.

[0004] Whether it is for the improvement of the performance of the turbine control system itself or for the analysis of the power system, it must be based on an accurate and reliable mathematical model. The testing and dynamic modeling of the four major parameters of the power system (including the generator, excitation system, prime mover and control system, and comprehensive load model) has become an urgent problem to be solved. The prior art has little research on the modeling method and state optimization for turbines and control systems, one of the four major parameters, especially for power system analysis and calculation. There is no complete, practical and unified method. The mathematical model of the turbine controller used in the power system analysis software is very different from the actual controller in terms of model structure, and it is far from meeting the needs of accurate analysis of the power turbine system. The prior art algorithm converges slowly, and the disadvantage model of premature local optimality cannot achieve parameter optimization.

[0005] The problems that need to be solved in the existing technology of turbine control modeling and state optimization and the key technical difficulties of this application include:

[0006] (1) Modeling and parameter testing of the turbine control system is a difficult problem in power generation control that needs to be solved urgently. The existing technology cannot realize the testing and dynamic modeling of the four major parameters of the power system (including generator, excitation system, prime mover and control system, and comprehensive load model) based on an accurate and reliable mathematical model. The existing technology has little research on the modeling method and state optimization for turbines and control systems, one of the four major parameters, especially for power system analysis and calculation. There is no complete, practical and unified method. The mathematical model of the turbine controller used in the power system analysis software is very different from the actual controller in operation, even in the model structure, which is far from meeting the needs of accurate analysis of the power turbine system. The existing algorithm converges slowly, the local optimum is too early, and the defect model cannot realize parameter optimization. Since the turbine generator set often performs frequency regulation and peak regulation tasks in the power grid, it is frequently started and stopped. At present, its performance is not good enough and the automation level is not high, which directly affects the normal operation of the unit. The poor performance of the turbine control system is very unfavorable to the power quality and safe and reliable operation of the power system.

[0007] (2) The existing technology of hydropower plant unit control can no longer meet the ever-increasing requirements of power users for power quality. The control quality, control stability boundary, turbine power generation efficiency, and optimal control of the hydro-mechanical-electrical combined control system of the turbine cannot fully meet the requirements. The traditional genetic algorithm cannot guarantee polynomial time convergence, so it is difficult to avoid premature convergence. The global search ability of the solution space is poor, and it is easy to fall into premature convergence and genetic stability problems. The changes in generator operating conditions and loads affect the parameters of the generator model. With the expansion of the production scale of hydropower plants and the widespread application of medium and large units, the increase in the complexity of power systems, and the increasing number of various control methods, the control and stability problems of turbines are becoming increasingly complex. It is not limited to the control quality requirements of a single control system, but also the coupling effects between various control processes must be considered. The existing technology cannot meet these requirements, and it is impossible to establish an accurate mathematical model of the turbine control system, realize power system analysis and calculation, and improve the control performance of the regulator, which affects the power supply quality, economic benefits and safe operation of the hydropower plant and its entire power system.

[0008] (3) The prior art lacks modeling and analysis of the turbine control system, lacks mathematical models of the water diversion system, mathematical models of the turbine controller, lacks modeling and analysis of the main hydraulic amplification element and the electro-hydraulic follow-up system, and lacks the establishment of a unified model of the controller with output speed limit and pure delay of the follow-up system. It is impossible to build a unified model of the turbine and the water diversion system and a unified model of the parallel PID controller to support the identification of the turbine control system model; lacks the identification of the turbine control system model, cannot realize the identification of the controller model and the identification of the monotonic turbine and its water diversion system model, and has not established the overall control and identification model of the turbine control system; cannot optimize the state of the turbine control system model based on the improved multi-agent, lacks parameter identification based on the improved multi-agent, cannot build the controller model, and cannot build the turbine and its water diversion pipeline model. The prior art turbine generator control modeling and state optimization algorithm have poor effects, cannot timely and accurately diagnose turbine power generation failures, cannot handle faulty equipment in a timely manner, and is not conducive to ensuring the safety and stability of the power system. Summary of the invention

[0009] Based on the characteristics of the turbine controller and the controlled object, the application establishes a mathematical model of the turbine control system, introduces a step disturbance signal in the construction of the model, and uses multi-agents to perform model identification on the controller part of the control system and the turbine and its water diversion system part; it can ensure polynomial time convergence and avoid premature convergence. The application proposes an improved multi-agent. This algorithm increases the global search capability in the solution space and uses external forces to break the local solution. The essence is to simulate the catastrophic phenomenon that a large number of species are extinct while individual species survive due to the biological renewal process, retain the current best solution, randomly generate other individuals again, and avoid falling into premature convergence and genetic stability problems. The results confirm the effectiveness of the algorithm. Establishing an accurate mathematical model of the turbine control system is of great significance for the analysis and calculation of the power system and the improvement of the control performance of the controller. The control system greatly improves the power generation efficiency and quality of the turbine generator set. Its performance improvement has a positive effect on the power supply quality, economic benefits and safe operation of the hydropower plant and its entire power system.

[0010] In order to achieve the above technical effects, the technical solutions adopted in this application are as follows:

[0011] The multi-agent modeling and state optimization method of hydro-generator control is used to establish the mathematical model of the hydro-turbine control system based on the characteristics of the hydro-turbine controller and the controlled object. Step disturbance signals are introduced into the model, and multi-agents are used to perform model identification on the controller part of the control system and the hydro-turbine and its water diversion system part.

[0012] A. Modeling and analysis of turbine control system: mathematical models of water diversion system and turbine controller are established respectively. Modeling and analysis of turbine controller includes modeling and analysis of main hydraulic amplification element and electro-hydraulic servo system, establishment of unified model of controller with output speed limit and pure delay of servo system, construction of unified model of turbine and water diversion system and unified model of parallel PID controller, and support identification of turbine control system model;

[0013] B. Model identification of hydraulic turbine control system: It is divided into model identification of the controller and model identification of monotonic hydraulic turbine and its water diversion system, which include both discrete model and continuous model. Model identification of monotonic hydraulic turbine and its water diversion system includes linear PID controller identification model and PID controller identification model with relay speed limit. Finally, the master control and identification model of hydraulic turbine control system is established.

[0014] C State optimization of the turbine control system model based on improved multi-agent: First, parameter identification based on improved multi-agent, including: improved multi-agent rule definition, multi-agent improvement method, improved multi-agent operator construction; second, regulator model construction; third, turbine and water diversion pipeline model construction.

[0015] Preferably, the water diversion system is modeled and analyzed: if the water flow in the pipeline is disturbed and accompanied by changes in flow and head, and the elastic deformation of the water body and the pipe wall cannot be ignored on the water hammer pressure value and the entire transition process, the elastic deformation propagates the disturbance along the pipeline in the form of a finite wave speed α, and is still solved from the motion equation and continuity equation of the fluid, ignoring the friction loss and minor terms in the pipeline, and deriving the continuity equation and motion equation of a fluid isolation body in the pipeline:

[0016] Continuity equation:

[0017] Equations of motion:

[0018] Q is the flow rate of a certain flow section in the pipeline at time t (m 3 / S), H is the water head of a certain flow section in the pipeline at time t (m), x is the distance from the corresponding flow section to the specified point (m), D is the pipeline diameter (m), and F is the cross-sectional area of ​​the pipeline (m 2 ), a is the water hammer wave velocity (m / S), f is the pipeline hydraulic friction resistance coefficient, and we get:

[0019]

[0020] Assume that the distance from the turbine inlet to the reservoir water intake is L, the relative value of the head increment is h, and the water hammer phase length is T. r =2L / a, and by performing Laplace transformation on equations 3 and 4, the relationship between the flow rate and the water head at the turbine inlet is obtained as follows:

[0021]

[0022] In the formula, h w is the pipeline constant, T r =2L / a is the water hammer phase; a is the water hammer wave velocity, and the hyperbolic tangent function in the formula is expanded using series as follows:

[0023]

[0024] Expand the first few items to get:

[0025]

[0026] Considering elastic water hammer, take the first two terms in the numerator and the first three terms in the denominator, we get:

[0027]

[0028] If only rigid water hammer is considered, the numerator and denominator can only take the first term, that is:

[0029]

[0030] A rigid model is used to identify pressure pipes less than 600-800 as small fluctuations. Elastic water is calculated in some necessary cases including tailwater pipes.

[0031] Preferably, the mathematical model of the main hydraulic amplification element: the main hydraulic amplification element of the regulator is a hydraulic amplification stage composed of a main pressure regulating valve and a main servomotor, which is considered as a simple integral link, namely:

[0032]

[0033] Where, Y(s) is the position of the relay, S(s) is the opening of the main pressure regulating valve, T y is the relay time constant, and the main hydraulic amplifier element is connected in series as an integral link and a second-order link.

[0034] Preferably, the linear mathematical model of the electro-hydraulic servo system adopts the structure of a controller + electro-hydraulic servo system, the required control law is generated by an analog circuit or a microcomputer, and the hydraulic amplification part is closed by itself to form a relatively independent electro-hydraulic servo system, assuming that: 1) all elements with integral properties except the main hydraulic amplification element are closed by feedback; 2) the time constants of the remaining elements in all hydraulic amplification parts are much smaller than the main relay time constant; 3) all transfer coefficients from the electro-hydraulic converter input to the auxiliary relay output are taken into account in the relay time constant T; 4) the influence of high-order factors and nonlinear factors are ignored;

[0035] Y C is the input of the servo system, is the electrical signal output by the regulator, Y is the position of the relay, that is, the output of the servo system, T y is the relay time constant. In static state, Y follows Y C , and there is a one-to-one correspondence with it, which is a follow-up system, and the corresponding transfer function is:

[0036]

[0037] Establish a mathematical model of the electro-hydraulic servo system with output speed limitation and pure delay: introduce the corresponding saturated nonlinear link and pure delay link, add the setting of delay link and nonlinear link, where the upper and lower saturation points of the saturated nonlinear link before the integrator are set at s max and -s max They are used to adjust the maximum movement speed of the relay in the opening and closing directions respectively, set 0 and 1 to simulate the upper and lower limit numerical requirements of the nonlinear link, simulate the available travel range of the relay, and the pure delay link introduced at the system input end simulates the delay characteristics of the system.

[0038] Preferably, the turbine controller identification model: the controller mathematical model is composed of the controller and the servo system model, which are classified as a whole, and the input excitation and output of the identification model are taken as the frequency input of the controller and the output displacement of the relay;

[0039] 1-Linear PID controller identification model

[0040] The linear PID controller identification model to be identified is:

[0041]

[0042] b1, b2, a1, a2, b p , T y are the model coefficients, or:

[0043]

[0044] Y(s) is the position of the relay, S(s) is the opening of the main pressure regulating valve. After the model identification is completed, β1, β2, α0, α1, α2, and α3 can all be considered known. First, the formula:

[0045] b2=T d T n , b1=T d +T n

[0046] a2=T′ n T d (b p +b t ), a1=b p T′ n +T d (b p +b t ) Formula 13

[0047] Substitute into the relationship: β1=b1,β2=b2,α0=b p , α1=b p T y +a1, α2 = a1T y +a2 α3=a2T y In the result:

[0048] β1=T d +T n

[0049] β2=T d T n

[0050] α0=b p

[0051] α1=bp T y +b p T′ n +T d (b p +b t )

[0052] α2=(b p T′ n +T d (b p +b t ))T y +T′ n T d (b p +b t )

[0053] α3=T y T′ n T d (b p +b t ) Formula 14

[0054] For the intermediate actuator, solve the above nonlinear equations to obtain its parameter b t 、T d 、b p 、T y 、T n and T′ n , if expressed as the parameters of a parallel controller, is calculated by the following formula:

[0055]

[0056] If we consider a pure time delay T D , the pure delay time added by the parameter to be identified is independent, and the calculation of the controller parameters still uses Equation 15;

[0057] 2-PID controller identification model with relay speed limit

[0058] The linear part of the regulator model is:

[0059]

[0060] The servo system becomes a saturated nonlinear system. After the model identification is completed, b2, b1, a2, a1, b p 、T y , S max and -S max Assuming it is known, calculate the corresponding controller parameters and solve them by the following formula:

[0061] b2=T d T n

[0062] b1=T d +T n

[0063] a2=T′ n T d (b p +b t )

[0064] a1=b p T′ n +T d (b p +b t ) Formula 17

[0065] For the parallel PID controller structure, its parameters are also obtained by formula 15.

[0066] Preferably, the identification model of the monotonic turbine and the water diversion system: If the monotonic turbine is considered to be operated in a grid-connected state and the grid frequency fluctuation is assumed to be negligible, its identification model is written in a unified model form as follows:

[0067]

[0068] Where:

[0069] a0=1 / e my

[0070] a1=e qh T w / e my

[0071] b1=-e y T w Formula 19

[0072] Turbine torque M t and flow rate Q with guide vane opening a y , Blade opening Changes in water head H and speed n, M r , Q r 、n r and H r are the values ​​of torque, flow, speed and water head under rated working conditions, respectively. M and M is the maximum value of the guide vane opening and the servomotor displacement, is the maximum blade opening. After the model identification is completed, a0, a1 and b1 are considered known;

[0073] Parameter calculation of the turbine and its water diversion system: There are only three independent coefficients a0, a1, b1 in the model, and three related equations are listed. The number of actual parameters of the corresponding turbine and its water diversion system is greater than 3, and only the transmission coefficient of the opening to the torque e can be determined. my =1 / a0.

[0074] Preferably, the improved multi-agent rule definition is: when an optimal solution is obtained, the optimal solution will be retained, and then new individuals will be randomly generated. In the next update, there will be individuals with more diverse meanings than the previous individuals. The small scale of the intelligent agent obtains the diversity of the large scale of the intelligent agent, breaking away from the limitation of the local optimal solution;

[0075] According to the catastrophic requirements of the biological renewal history of nature, the intelligent agent is processed by multi-agent. This intelligent agent is the optimal solution for the stage. The specific process of improving the multi-agent includes:

[0076] (a) Initialize the agent and target value;

[0077] (b)) Evaluate fitness and perform genetic manipulations;

[0078] (c) Determine whether the requirements for improving multi-agents are met and perform multi-agent operations;

[0079] (d) Perform multi-agent operations and fitness evaluation at this stage;

[0080] (f) The number of multi-agents increases by 1;

[0081] (g) If the result meets the expected situation, then end; otherwise, save the best solution and continue the disaster;

[0082] The improved agent is used to improve the solution space, and the local optimal solution is reserved in advance when performing genetic update operations to prevent random algorithm search.

[0083] Preferably, the multi-agent improvement method is: increasing the agent scale M improves the global performance of the multi-agent, but the cost is a corresponding increase in the solution time, and the solution time T is proportional to the product of M and the iteration number k, that is: T = MKT, where T0 is the average calculation time to generate a new individual through genetic operations and fitness evaluation;

[0084] Based on premature convergence, the current optimal solution before the catastrophe Implicitly, the local optimal characteristics of the current agent in stage i, after the multi-agent operation occurs, Save as individual 0, Its fitness is the same as other randomly generated individuals The fitness of is at a higher position than that of the previous one, and the tournament selection mechanism is adopted to ensure that it can participate in the next round of updates;

[0085] Specifically, before and after the multi-agent operation is performed to generate the first generation of agents, among the 2M-1 individuals in two consecutive stages, the advanced direction and optimal traits of the M individuals in the i-th stage are represented. It is easier to be selected into the breeding pool than the other M-1 individuals in stage i. The tournament selection mechanism ensures It will not be lost in the first iteration after the catastrophe operation, thus ensuring that the M individuals in the previous stage can be implicitly updated in this stage. The role played by the retained best solution in the new round of update process is the same as the effect of the M individuals before the operation directly joining in the update operation, so that the algorithm implicitly expands the scale of intelligent agents without changing the scale of operation.

[0086] Assuming the number of disasters is c, and the number of update iterations in each stage is k', then in each stage, the calculation time is: T (i) =Mk′T0, i=0, 1, 2, ..., C, total solution time: T=M(C+1)k′T0;

[0087] The implicit operation scale of each stage is M+(M-1)i. If all implicit agents are allowed to participate in the update, the calculation time of the i-th stage is: T′ (i) =[M+(M-1)i]k′T0, total solution time:

[0088] get: When C>0, T<T', and the larger C is, the greater the difference between the two. It will not be lost in the first iteration after the multi-agent operation. The multi-agent operator can give full play to the implicit parallelism of multi-agents and improve the efficiency of multi-agents.

[0089] Preferably, the operator construction of the improved multi-agent is: when the genetic operation update condition does not meet the expected situation, the improved multi-agent catastrophic operation is performed, and the binary search coding is adopted based on the stability of the mutation and the feasibility of the operation;

[0090] This application uses the minimum fitness value to represent the global optimal fitness, sets a pregen-c as the stagnant generation, and performs multi-agent operation if the current generation is greater than the set generation, ensuring that the generation is between the second generation and the maximum generation, and the current optimal solution is less than the historical optimal solution, which is necessary to trigger the multi-agent operation. After the multi-agent operation, while retaining the optimal solution, set an arbitrary ratio to replace the original part, set a number rounding environment, and prevent the occurrence of decimal parts;

[0091] After each multi-agent operation is completed, the new agent must be initialized, space must be set to save the new fitness function, space must be reserved to fill in numbers, a new objective function must be defined, and the Griewank function must be selected as the expression of the objective function. The new agents and their fitness after the disaster are arranged in descending order to facilitate the next genetic and multi-agent operations. Each multi-agent operation has a disaster mark for easy observation until the set maximum generation of genetic operations is reached. Starting from the overall update of the agent, the convergence and stability of the algorithm are taken into consideration.

[0092] Preferably, the controller model is constructed as follows: first, a model parameter initialization program is constructed to initialize various parameters of the controller model and the turbine and water diversion system model, and the model construction is started under SIMULINK. All the data recorded by the construction are stored in the file construction result file PC.mat in the form of a matrix, and the variable name is Rdt. The data contained in the file is provided to the identification algorithm;

[0093] The identification model is divided into two parts, namely, the controller model (controller + follow-up system) and the turbine and water diversion system model. The controller model includes PI-type controller and PID-type controller. The identification data used by the controller model are frequency disturbance and relay displacement, and the identification data used by the turbine and water diversion system model are relay displacement and turbine output torque. The corresponding ideal model of the controller is:

[0094]

[0095] A unified model discrimination criterion is introduced (all the following methods adopt this criterion), and the evaluation function value calculation method is:

[0096]

[0097] The main parameters of multi-agent are set as follows: agent size = 80; crossover probability = 0.7; mutation probability based on random traversal sampling strategy; maximum genetic generations = 100, and an optimal solution is retained in each generation of mutation.

[0098] Compared with the prior art, the innovations and advantages of this application are:

[0099] (1) Based on the characteristics of the turbine controller and the controlled object, this application establishes a mathematical model of the turbine control system, introduces a step disturbance signal into the model, and uses multi-agents to perform model identification on the controller part of the control system and the turbine and its water diversion system part; it can ensure polynomial time convergence and avoid premature convergence. This application proposes an improved multi-agent algorithm, which increases the global search capability in the solution space and uses external forces to break the local solution. Its essence is to simulate the catastrophic phenomenon that a large number of species are extinct while some species survive due to the biological renewal process, retain the current best solution, randomly generate other individuals, and avoid falling into premature convergence and genetic stability problems. The results confirm the effectiveness of the algorithm. Establishing an accurate mathematical model of the turbine control system is of great significance for the analysis and calculation of the power system and the improvement of the control performance of the controller. The control system greatly improves the power generation efficiency and quality of the turbine generator set. Its performance improvement has a positive effect on the power supply quality, economic benefits and safe operation of the hydropower plant and its entire power system.

[0100] (2) This application proposes a method for modeling and analyzing a hydraulic turbine control system. The mathematical model of the water diversion system and the mathematical model of the hydraulic turbine controller are established respectively. The modeling and analysis of the hydraulic turbine controller includes the modeling and analysis of the main hydraulic amplification element and the electro-hydraulic follow-up system, the establishment of a unified model of the controller with output speed limit and pure delay of the follow-up system, the construction of a unified model of the hydraulic turbine and the water diversion system and a unified model of the parallel PID controller, and the support of the model identification of the hydraulic turbine control system; the modeling and parameter testing of the hydraulic turbine control system are realized. On the basis of an accurate and reliable mathematical model, the testing and dynamic modeling of the four major parameters of the power system (including the generator, the excitation system, the prime mover and the control system, and the comprehensive load model) are realized, and a complete, practical and unified method is proposed. It meets the needs of accurate analysis of the electric hydraulic turbine system, the algorithm converges quickly, solves the disadvantage of the local optimal premature model, and realizes parameter optimization. The hydraulic turbine generator set often performs frequency regulation and peak regulation tasks in the power grid, and is frequently started and shut down. The improvement of its performance and the improvement of the automation level are conducive to the normal operation of the unit. The improvement of the performance of the hydraulic turbine control system is very useful for the power quality and safe and reliable operation of the power system.

[0101] (3) The present application proposes the identification of the model of a water turbine control system, which is divided into the identification of the model of the regulator and the identification of the model of a monotonic water turbine and its water diversion system. The identification of the model of the monotonic water turbine and its water diversion system includes a linear PID controller identification model and a PID controller identification model with a relay speed limit. Finally, a general control and identification model of the water turbine control system is established. The state optimization of the water turbine control system model based on the improved multi-agent is realized. Through the parameter identification, the construction of the regulator model, and the construction of the water turbine and its water diversion pipeline model based on the improved multi-agent, the requirements of the power consumption department for the continuous improvement of the power quality are met. The control quality, the control stability boundary, the power generation efficiency of the water turbine, and the optimal control of the water-machine-electric combined system of the water turbine control system fully meet the requirements, ensuring polynomial time convergence, strong global search capability of the solution space, and no premature convergence and genetic stability problems. In order to meet the requirements of the expansion of the production scale of hydropower plants and the widespread application of medium and large units, the increasing complexity of power systems and the increasing number of various control methods, the control and stability problems of turbines are becoming increasingly complex. This application is not only limited to the requirements for the control quality of a single control system, but also takes into account the coupling effects between various control processes, establishes an accurate mathematical model of the turbine control system, and realizes power system analysis and calculation and improves the control performance of the regulator. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] Figure 1 It is a simplified block diagram of the electro-hydraulic servo system.

[0103] Figure 2 It is a simplified block diagram of the corresponding saturated nonlinear link of the follow-up system.

[0104] Figure 3 It is a unified model diagram of the speed regulator considering the output speed limit and pure delay of the follow-up system.

[0105] Figure 4 This is the identification model diagram of the PID controller with relay speed limit.

[0106] Figure 5 It is a schematic diagram summarizing the identification results of the PID controller.

[0107] Figure 6 It is a schematic diagram summarizing the identification results of the turbine and its water diversion system. DETAILED DESCRIPTION

[0108] The following, in conjunction with the accompanying drawings, further describes the technical solution of the multi-agent modeling and state optimization method for hydro-generator control provided in the present application, so that technicians in this field can better understand the present application and implement it.

[0109] The control object of the turbine control system is the turbine generator set, which together with the turbine controller as the system controller constitutes a closed-loop control system. Its performance is closely related to the power supply quality, economic benefits and safe operation of the hydropower plant and its entire power system. Establishing an accurate mathematical model of the turbine control system is of great significance for the analysis and calculation of the power system and the improvement of the control performance of the controller.

[0110] Traditional genetic algorithms cannot guarantee polynomial time convergence and it is difficult to avoid premature convergence. Therefore, this application proposes an improved multi-agent algorithm, which increases the global search capability in the solution space and uses external forces to break local solutions. Its essence is to simulate the catastrophic phenomenon of biological renewal process that leads to the extinction of a large number of species while individual species survive, retain the current best solution, randomly generate other individuals again, and avoid falling into premature convergence and genetic stability problems. The results confirm the effectiveness of the algorithm.

[0111] 1. Modeling and analysis of turbine control system

[0112] (I) Modeling and analysis of water diversion system

[0113] If the water flow in the pipeline is disturbed and accompanied by changes in flow rate and water head, and the elastic deformation of the water body and the pipe wall cannot be ignored in the water hammer pressure value and the entire transition process, the elastic deformation will propagate the disturbance along the pipeline in the form of a finite wave speed α. Still starting from the motion equation and continuity equation of the fluid, ignoring the friction loss and minor terms in the pipeline, the continuity equation and motion equation of a fluid isolation body in the pipeline are derived:

[0114] Continuity equation:

[0115] Equations of motion:

[0116] Q is the flow rate of a certain flow section in the pipeline at time t (m 3 / S), H is the water head of a certain flow section in the pipeline at time t (m), x is the distance from the corresponding flow section to the specified point (m), D is the pipeline diameter (m), and F is the cross-sectional area of ​​the pipeline (m 2 ), a is the water hammer wave velocity (m / S), f is the pipeline hydraulic friction resistance coefficient, and we get:

[0117]

[0118] Assume that the distance from the turbine inlet to the reservoir water intake is L, the relative value of the head increment is h, and the water hammer phase length is T. r =2L / a, and by performing Laplace transformation on equations 3 and 4, the relationship between the flow rate and the water head at the turbine inlet is obtained as follows:

[0119]

[0120] In the formula, h w is the pipeline constant, T r =2L / a is the water hammer phase; a is the water hammer wave velocity, and the hyperbolic tangent function in the formula is expanded using series as follows:

[0121]

[0122] Expand the first few items to get:

[0123]

[0124] Considering elastic water hammer, take the first two terms in the numerator and the first three terms in the denominator, we get:

[0125]

[0126] If only rigid water hammer is considered, the numerator and denominator can only take the first term, that is:

[0127]

[0128] A rigid model is used to identify pressure pipes less than 600-800 as small fluctuations. Elastic water is calculated in some necessary cases including tailwater pipes.

[0129] (II) Modeling and analysis of turbine controller

[0130] The active power output of the hydro-generator unit is continuously adjusted according to the changes in the unit load to maintain the unit speed (frequency) within the specified range, in order to monitor the system status, predict the system performance, diagnose system faults, and establish an accurate regulator model.

[0131] 1. Modeling and analysis of main hydraulic amplifier components and electro-hydraulic servo system

[0132] (1) Mathematical model of the main hydraulic amplifier

[0133] The main hydraulic amplification element of the regulator is a hydraulic amplification stage composed of the main pressure regulating valve and the main relay, which can be seen as a simple integral link, namely:

[0134]

[0135] Where, Y(s) is the position of the relay, S(s) is the opening of the main pressure regulating valve, T y is the relay time constant, and the main hydraulic amplifier element is connected in series as an integral link and a second-order link.

[0136] (2) Linear mathematical model of electro-hydraulic servo system

[0137] The structure of the controller + electro-hydraulic servo system is adopted. The required control law is generated by the analog circuit or microcomputer, and the hydraulic amplification part is closed by itself, forming a relatively independent electro-hydraulic servo system. It is assumed that: 1) except for the main hydraulic amplification element, all elements with integral properties are closed by feedback; 2) the time constants of the remaining elements in all hydraulic amplification parts are much smaller than the main relay time constant; 3) all the transfer coefficients from the electro-hydraulic converter input to the auxiliary relay output are taken into account in the relay time constant T; 4) the influence of high-order factors and nonlinear factors are ignored. Draw a simplified block diagram of the servo system as shown below: Figure 1 shown.

[0138] In the figure, Y C is the input of the servo system, is the electrical signal output by the regulator, Y is the position of the relay, that is, the output of the servo system, T y is the relay time constant. In static state, Y follows Y C , and there is a one-to-one correspondence with it, which is a follow-up system, and the corresponding transfer function is:

[0139]

[0140] (3) Establishing a mathematical model of the electro-hydraulic servo system with output speed limitation and pure delay

[0141] Introduce the corresponding saturation nonlinear link and pure delay link, such as Figure 2 As shown. Add the delay link and nonlinear link settings, where the upper and lower saturation points of the saturation nonlinear link before the integrator are set at s max and -s max They are used to adjust the maximum movement speed of the relay in the opening and closing directions respectively, set 0 and 1 to simulate the upper and lower limit numerical requirements of the nonlinear link, simulate the available travel range of the relay, and the pure delay link introduced at the system input end simulates the delay characteristics of the system.

[0142] 2. Establish a unified model of the controller for output speed limit and pure delay of the follow-up system

[0143] Establish the output speed limit and pure delay of the electro-hydraulic servo system. Figure 2 The saturated nonlinear link (speed limit model) and the follow-up system model of the pure delay link are combined with the unified model of the turbine controller to form a more suitable unified PID model of the turbine controller under large disturbance conditions, such as Figure 3 shown.

[0144] 2. Model Identification of Turbine Control System

[0145] (I) Identification model of water turbine regulator

[0146] The mathematical model of the controller is composed of the controller and the servo system model, which are classified as a whole. The input excitation and output of the identification model are taken as the frequency input (frequency measurement or frequency setting) of the controller and the output displacement of the relay.

[0147] 1. Linear PID controller identification model

[0148] The linear PID controller identification model to be identified is:

[0149]

[0150] b1, b2, a1, a2, b p 、T y are the model coefficients, or:

[0151]

[0152] Y(s) is the position of the relay, S(s) is the opening of the main pressure regulating valve. After the model identification is completed, β1, β2, α0, α1, α2, and α3 can all be considered known. First, the formula:

[0153] b2=T d T n , b1=T d +T n

[0154] a2=T′ n T d (b p +b t ), a1=b p T′ n +T d (b p +b t ) Formula 13

[0155] Substitute into the relationship: β1=b1,β2=b2,α0=b p , α1=b p T y +a1, α2 = a1T y +a2 α3=a2T y In the result:

[0156] β1=T d +T n

[0157] β2=T d T n

[0158] α0=b p

[0159] α1=bp T y +b p T′ n +T d (b p +b t )

[0160] α2=(b p T′ n +T d (b p +b t ))T y +T′ n T d (b p +b t )

[0161] α3=T y T′ n T d (b p +b t ) Formula 14

[0162] For the intermediate actuator, solve the above nonlinear equations to obtain its parameter b t , T d 、b p , T y , T n and T′ n , if expressed as the parameters of a parallel controller, is calculated by the following formula:

[0163]

[0164] If we consider a pure time delay T D , the pure delay time added by the parameter to be identified is independent, and the calculation of the controller parameters still uses Equation 15.

[0165] 2. PID controller identification model with relay speed limit

[0166] The identification model of PID controller with relay speed limit is as follows: Figure 4 The linear part of the regulator model is:

[0167]

[0168] The servo system becomes a saturated nonlinear system. After the model identification is completed, b2, b1, a2, a1, b p , T y , S max and -S max Assuming it is known, calculate the corresponding controller parameters and solve them by the following formula:

[0169] b2=T d T n

[0170] b1=T d +T n

[0171] a2=T′ n T d (b p +b t )

[0172] a1=b p T′ n +T d (b p +b i ) Formula 17

[0173] For the parallel PID controller structure, its parameters are also obtained by formula 15.

[0174] (II) Identification model of monotonic turbine and water diversion system

[0175] If the monotonic turbine is considered to be running in the grid-connected state and the grid frequency fluctuation is assumed to be negligible, its identification model can be written in the form of a unified model:

[0176]

[0177] Where:

[0178] a0=1 / e my

[0179] a1=e qh T w / e my

[0180] b1=-e y T w Formula 19

[0181] Turbine torque M t and flow rate Q with guide vane opening a y , Blade opening Changes in water head H and speed n, M r , Q r 、n r and H r are the values ​​of torque, flow, speed and water head under rated working conditions, respectively. M and M is the maximum value of the guide vane opening and the servomotor displacement, is the maximum blade opening. After the model identification is completed, a0, a1 and b1 are considered known;

[0182] Parameter calculation of the turbine and its water diversion system: There are only three independent coefficients a0, a1, b1 in the model, and three related equations are listed. The number of actual parameters of the corresponding turbine and its water diversion system is greater than 3. The actual parameters cannot be fully determined by the model parameters. Only the transmission coefficient of the opening to the torque e can be determined. my =1 / a0.

[0183] 3. State optimization of turbine control system model based on improved multi-agent

[0184] 1. Parameter identification based on improved multi-agent

[0185] 1. Improved multi-agent rule definition

[0186] When an optimal solution is obtained, it will be retained, and then new individuals will be randomly generated. In the next update, there will be individuals with more diversity than the previous individuals. The small scale of intelligent agents obtains the diversity of the large scale of intelligent agents, breaking away from the limitations of the local optimal solution.

[0187] According to the catastrophic requirements of the biological renewal history of nature, the intelligent agent is processed by multi-agent. This intelligent agent is the optimal solution for the stage. The specific process of improving the multi-agent includes:

[0188] (a) Initialize the agent and target value;

[0189] (b)) Evaluate fitness and perform genetic manipulations;

[0190] (c) Determine whether the requirements for improving multi-agents are met and perform multi-agent operations;

[0191] (d) Perform multi-agent operations and fitness evaluation at this stage;

[0192] (f) The number of multi-agents increases by 1

[0193] (g) If the result meets the expected situation, then end; otherwise, save the best solution and continue the disaster;

[0194] Improved agents are used to increase the solution space, and local optimal solutions are reserved in advance when performing genetic update operations to prevent random algorithm searches and ensure the stability of multi-agents.

[0195] 2. Multi-agent Improvement Method

[0196] Increasing the agent size M improves the global performance of multi-agents, but at the cost of a corresponding increase in solution time, and the solution time T is proportional to the product of M and the number of iterations k, that is: T = MKT, where T0 is the average computational time to generate a new individual through genetic operations and fitness evaluation;

[0197] Based on premature convergence, the current optimal solution before the catastrophe Implicitly, the local optimal characteristics of the current agent in stage i, after the multi-agent operation occurs, Save as individual 0, Its fitness is the same as other randomly generated individuals The fitness of is at a higher position than that of the previous one, and the tournament selection mechanism is adopted to ensure that it can participate in the next round of updates;

[0198] Specifically, before and after the multi-agent operation is performed to generate the first generation of agents, among the 2M-1 individuals in two consecutive stages, the advanced direction and optimal traits of the M individuals in the i-th stage are represented. It is easier to be selected into the breeding pool than the other M-1 individuals in stage i. The tournament selection mechanism ensures It will not be lost in the first iteration after the catastrophe operation, thus ensuring that the M individuals in the previous stage can be implicitly updated in this stage. The role played by the retained best solution in the new round of update process is the same as the effect of the M individuals before the operation directly joining in to participate in the update operation, so that the algorithm implicitly expands the scale of intelligent agents while keeping the scale of operation unchanged.

[0199] Assuming the number of disasters is c, and the number of update iterations in each stage is k', then in each stage, the calculation time is: T (i) =Mk′T0, i=0, 1, 2, ..., C, total solution time: T=M(C+1)kT0;

[0200] The implicit operation scale of each stage is M+(M-1)i. If all implicit agents are allowed to participate in the update, the calculation time of the i-th stage is: T′ (i) =[M+(M-1)i]k′T0, total solution time:

[0201] get: When C>0, T<T', and the larger C is, the greater the difference between the two. It will not be lost in the first iteration after the multi-agent operation. The multi-agent operator can give full play to the implicit parallelism of multi-agents and improve the efficiency of multi-agents.

[0202] 3. Improve the construction of multi-agent operators

[0203] When the genetic operation update conditions do not meet the expected situation, the improved multi-agent disaster operation is performed, and binary search coding is used based on the stability of mutation and the feasibility of operation;

[0204] This application uses the minimum fitness value to represent the global optimal fitness, sets a pregen-c as the stagnant generation, and performs multi-agent operation if the current generation is greater than the set generation, ensuring that the generation is between the second generation and the maximum generation, and the current optimal solution is less than the historical optimal solution, which is necessary to trigger the multi-agent operation. After the multi-agent operation, while retaining the optimal solution, set an arbitrary ratio to replace the original part, set a number rounding environment, and prevent the occurrence of decimal parts;

[0205] After each multi-agent operation is completed, the new agent must be initialized, space must be set to save the new fitness function, space must be reserved to fill in numbers, a new objective function must be defined, and the Griewank function must be selected as the expression of the objective function. The new agents and their fitness after the disaster are arranged in descending order to facilitate the next genetic and multi-agent operations. Each multi-agent operation has a disaster mark for easy observation until the set maximum generation of genetic operations is reached. Starting from the overall update of the agent, the convergence and stability of the algorithm are taken into consideration.

[0206] 2. Construction of the Regulator Model

[0207] First, a model parameter initialization program is constructed to initialize the various parameters of the regulator model and the turbine and water diversion system model. The model construction is started under SIMULINK. All the data recorded in the construction are stored in the file construction result file PC.mat in the form of a matrix. The variable name is Rdt. The data contained in this file is provided to the identification algorithm.

[0208] The identification model is divided into two parts, namely, the controller model (controller + follow-up system) and the turbine and water diversion system model. The controller model includes PI-type controller and PID-type controller. The identification data used by the controller model are frequency disturbance (as input data) and relay displacement (as output data). The identification data used by the turbine and water diversion system model are relay displacement (as input data) and turbine output torque (as output data). The corresponding ideal model of the controller is:

[0209]

[0210] A unified model discrimination criterion is introduced (all the following methods adopt this criterion), and the evaluation function value calculation method is:

[0211]

[0212] The main parameters of multi-agent are set as follows: agent size = 80; crossover probability = 0.7; mutation probability based on random traversal sampling strategy; maximum genetic generations = 100, and an optimal solution is retained in each generation of mutation.

[0213] (III) Construction of the model of the turbine and its water diversion pipeline

[0214] Parameters of the turbine and its water diversion system used during construction: T w = 1.5 seconds, e my =1.0,e qh =0.5, e y =1.0, corresponding to A0=1.0, A1=0.75, B1=-1.5, the corresponding ideal model of the turbine and its water diversion system is:

[0215]

[0216] Corresponding model:

[0217]

[0218] Corresponding VAal=0.059442.

[0219] (IV) Comparative analysis of identification results

[0220] In order to facilitate comparison, the identification results obtained by various methods are summarized based on the identification object;

[0221] (1) Summary of PID controller identification results

[0222] like Figure 5 Due to the inertia of the follow-up system and its inherent speed limit, the high-frequency components in the PID controller are nonlinearly weakened. By comparing the direct relationship between the three methods, it can be found that the improved multi-agent has obviously greatly improved the error. The improved multi-agent model can better reflect the characteristics of the actual turbine controller system and is more in line with the actual situation.

[0223] (2) Turbine and its water diversion system

[0224] like Figure 6 The identification results of the turbine and its water diversion system are summarized. For various methods (least squares identification, multi-agent model identification, and improved multi-agent model identification) and step disturbance signals, the obtained identification results are good. Under the improved multi-agent identification model, the results are very close to the ideal model, which further verifies the feasibility of the improved multi-agent. In summary, considering the feasibility of the field model, the time spent on the identification algorithm, the evaluation function value and error of the identification method, it is recommended to use the identification method based on the improved multi-agent for the model identification of the turbine and its water diversion system.

Claims

1. Multi-agent modeling and state optimization method for hydro-generator control, characterized by: Based on the characteristics of the turbine controller and the controlled object, a mathematical model of the turbine control system is established. A step disturbance signal is introduced into the model, and multi-agents are used to perform model identification on the controller part of the control system and the turbine and its water diversion system part. A. Modeling and analysis of turbine control system: mathematical models of water diversion system and turbine controller are established respectively. Modeling and analysis of turbine controller includes modeling and analysis of main hydraulic amplification element and electro-hydraulic servo system, establishment of unified model of controller with output speed limit and pure delay of servo system, construction of unified model of turbine and water diversion system and unified model of parallel PID controller, and support identification of turbine control system model; B. Model identification of hydraulic turbine control system: It is divided into model identification of the controller and model identification of monotonic hydraulic turbine and its water diversion system, which include both discrete model and continuous model. Model identification of monotonic hydraulic turbine and its water diversion system includes linear PID controller identification model and PID controller identification model with relay speed limit. Finally, the master control and identification model of hydraulic turbine control system is established. C State optimization of the turbine control system model based on improved multi-agent: First, parameter identification based on improved multi-agent, including: improved multi-agent rule definition, multi-agent improvement method, improved multi-agent operator construction; second, regulator model construction; third, turbine and water diversion pipeline model construction.

2. The multi-agent modeling and state optimization method for hydro-generator control according to claim 1 is characterized in that: Modeling and analysis of water diversion system: If the water flow in the pipeline is disturbed and accompanied by changes in flow rate and water head, and the elastic deformation of the water body and the pipe wall cannot be ignored in terms of the water hammer pressure value and the entire transition process, the elastic deformation propagates the disturbance along the pipeline in the form of a finite wave speed α. The solution is still based on the motion equation and continuity equation of the fluid, ignoring the friction loss and minor terms in the pipeline, and the continuity equation and motion equation of a fluid isolation body in the pipeline are derived: Q is the flow rate of a certain flow section in the pipeline at time t (m 3 / S), H is the water head of a certain flow section in the pipeline at time t (m), x is the distance from the corresponding flow section to the specified point (m), D is the pipeline diameter (m), and F is the cross-sectional area of ​​the pipeline (m 2 ), a is the water hammer wave velocity (m / S), f is the pipeline hydraulic friction resistance coefficient, and we get: Assume that the distance from the turbine inlet to the reservoir water intake is L, the relative value of the head increment is h, and the water hammer phase length is T. r =2L / a, and by performing Laplace transformation on equations 3 and 4, the relationship between the flow rate and the water head at the turbine inlet is obtained as follows: In the formula, h w is the pipeline constant, T r =2L / a is the water hammer phase; a is the water hammer wave velocity, and the hyperbolic tangent function in the formula is expanded using series as follows: Expand the first few items to get: Considering elastic water hammer, take the first two terms in the numerator and the first three terms in the denominator, and we get: If only rigid water hammer is considered, the numerator and denominator can only take the first term, that is: A rigid model is used to identify pressure pipes less than 600-800 as small fluctuations. In some necessary cases including tailwater pipes, elastic water is calculated.

3. The multi-agent modeling and state optimization method for hydro-generator control according to claim 1 is characterized in that: Mathematical model of the main hydraulic amplifier element: The main hydraulic amplifier element of the regulator is a hydraulic amplifier stage composed of the main pressure regulating valve and the main relay, which can be regarded as a simple integral link, namely: Where, Y(s) is the position of the relay, S(s) is the opening of the main pressure regulating valve, T y is the relay time constant, and the main hydraulic amplifier element is connected in series as an integral link and a second-order link.

4. The multi-agent modeling and state optimization method for hydro-generator control according to claim 1 is characterized in that: The linear mathematical model of the electro-hydraulic servo system: the structure of the controller + electro-hydraulic servo system is adopted, the required control law is generated by the analog circuit or microcomputer, and the hydraulic amplification part is closed by itself, forming a relatively independent electro-hydraulic servo system, assuming that: 1) except for the main hydraulic amplification element, all the elements with integral properties are closed by feedback; 2) the time constants of the remaining elements in all hydraulic amplification parts are much smaller than the main relay time constant; 3) all the transfer coefficients from the electro-hydraulic converter input to the auxiliary relay output are taken into account in the relay time constant T; 4) the influence of high-order factors and nonlinear factors are ignored; Y C is the input of the servo system, is the electrical signal output by the regulator, Y is the position of the relay, that is, the output of the servo system, T y is the relay time constant. In static state, Y follows Y C , and there is a one-to-one correspondence with it, which is a follow-up system, and the corresponding transfer function is: Establish a mathematical model of the electro-hydraulic servo system with output speed limitation and pure delay: introduce the corresponding saturated nonlinear link and pure delay link, add the setting of delay link and nonlinear link, where the upper and lower saturation points of the saturated nonlinear link before the integrator are set at s max and -s max They are used to adjust the maximum movement speed of the relay in the opening and closing directions respectively, set 0 and 1 to simulate the upper and lower limit numerical requirements of the nonlinear link, simulate the available travel range of the relay, and the pure delay link introduced at the system input end simulates the delay characteristics of the system.

5. The multi-agent modeling and state optimization method for hydro-generator control according to claim 1 is characterized in that: Identification model of water turbine controller: The mathematical model of the controller is composed of the controller and the servo system model, which are classified as a whole. The input excitation and output of the identification model are taken as the frequency input of the controller and the output displacement of the relay; 1-Linear PID controller identification model The linear PID controller identification model to be identified is: b1, b2, a1, a2, bp, Ty are model coefficients, or: Y(s) is the position of the relay, S(s) is the opening of the main pressure regulating valve. After the model identification is completed, β1, β2, α0, α1, α2, and α3 can all be considered known. First, the formula: b2=T d T n ,b1=T d +T n a2 = T′ n T d (b p +b t ), a1 = b p T′ n +T d (b p +b t ) Equation 13 Substitute into the relationship: β1=b1,β2=b2,α0=b p , α1=b p T y +a1, α2 = a1T y +a2 α3=a2T y In the result: β1=T d +T n β2=T d T n α0＝b p α1=b p T y +b p T′ n +T d (b p +b t ) α2=(b p T′ n +T d (b p +b t ))T y +T′ n T d (b p +b t ) α3 = T y T' n T d (b p +b t ) Equation 14 For the intermediate actuator, solve the above nonlinear equations to obtain its parameter b t , T d 、b p , T y , T n and T′ n , if expressed as the parameters of a parallel controller, is calculated by the following formula: If we consider a pure time delay T D , the pure delay time added by the parameter to be identified is independent, and the calculation of the controller parameters still uses Equation 15; 2-PID controller identification model with relay speed limit The linear part of the regulator model is: The servo system becomes a saturated nonlinear system. After the model identification is completed, b2, b1, a2, a1, b p , T y , S max , and -S max Assuming it is known, calculate the corresponding controller parameters and solve them by the following formula: b2=T d T n b1=T d +T n a2=T′ n T d (b p +b t ) a1 = b p T' n + T d (b p + b t ) Equation 17 For the parallel PID controller structure, its parameters are also obtained by formula 15.

6. The multi-agent modeling and state optimization method for hydro-generator control according to claim 1 is characterized in that: Identification model of monotonic turbine and water diversion system: If the monotonic turbine is considered to be running in the grid-connected state and the grid frequency fluctuation is assumed to be negligible, its identification model can be written in the form of a unified model: Where: a0=1 / e my a1=e qh T w / e my b1=-e y T w Formula 19 Turbine torque M t and flow rate Q with guide vane opening a y , Blade opening Changes in water head H and speed n, M r , Q r 、n r and H r are the values ​​of torque, flow, speed and water head under rated working conditions, respectively. M and M is the maximum value of the guide vane opening and the servomotor displacement, is the maximum blade opening. After the model identification is completed, a0, a1 and b1 are considered known; Parameter calculation of the turbine and its water diversion system: There are only three independent coefficients a0, a1, b1 in the model, and three related equations are listed. The number of actual parameters of the corresponding turbine and its water diversion system is greater than 3, and only the transmission coefficient of the opening to the torque e can be determined. my =1 / a0.

7. The multi-agent modeling and state optimization method for hydro-generator control according to claim 1 is characterized in that: Improved multi-agent rule definition: When an optimal solution is obtained, it will be retained, and then new individuals will be randomly generated. In the next update, there will be individuals with more diversity than the previous individuals. The small scale of agents can obtain the diversity of the large scale of agents, breaking away from the limitation of local optimal solutions. According to the catastrophic requirements of the biological renewal history of nature, the intelligent agent is processed by multi-agent. This intelligent agent is the optimal solution for the stage. The specific process of improving the multi-agent includes: (a) Initialize the agent and target value; (b)) Evaluate fitness and perform genetic manipulations; (c) Determine whether the requirements for improving multi-agents are met and perform multi-agent operations; (d) Perform multi-agent operations and fitness evaluation at this stage; (f) The number of multi-agents increases by 1; (g) If the result meets the expected situation, then end; otherwise, save the best solution and continue the disaster; The improved agent is used to expand the solution space, and the local optimal solution is reserved in advance when performing genetic update operations to prevent random algorithm search.

8. The multi-agent modeling and state optimization method for hydro-generator control according to claim 1 is characterized in that: Improved method for multi-agents: Increasing the size of agents M improves the global performance of multi-agents, but the cost is a corresponding increase in solution time, and the solution time T is proportional to the product of M and the number of iterations k, that is, T = MKT, where T0 is the average computational time to generate a new individual through genetic operations and fitness evaluation; Based on premature convergence, the current optimal solution before the catastrophe Implicitly, the local optimal characteristics of the current agent in stage i, after the multi-agent operation occurs, Save as individual 0, Its fitness is the same as other randomly generated individuals The fitness of is at a higher position than that of the previous one, and the tournament selection mechanism is adopted to ensure that it can participate in the next round of updates; Specifically, before and after the multi-agent operation is performed to generate the first generation of agents, among the 2M-1 individuals in two consecutive stages, the advanced direction and optimal traits of the M individuals in the i-th stage are represented. It is easier to be selected into the breeding pool than the other M-1 individuals in stage i. The tournament selection mechanism ensures It will not be lost in the first iteration after the catastrophe operation, thus ensuring that the M individuals in the previous stage can be implicitly updated in this stage. The role played by the retained best solution in the new round of update process is the same as the effect of the M individuals before the operation directly joining in the update operation, so that the algorithm implicitly expands the scale of intelligent agents without changing the scale of operation. Assuming the number of catastrophes is c, and the number of update iterations in each stage is k', then in each stage, the calculation time is: T(i) = Mk'T0, i = 0, 1, 2, ..., C, and the total solution time is: T = M(C+1)k'T0; The implicit operation scale of each stage is M+(M-1)i. If all implicit agents are allowed to participate in the update, the calculation time of the i-th stage is: Total solution time: get: When C>0, T<T', and the larger C is, the greater the difference between the two. It will not be lost in the first iteration after the multi-agent operation. The multi-agent operator can give full play to the implicit parallelism of multi-agents and improve the efficiency of multi-agents.

9. The multi-agent modeling and state optimization method for hydro-generator control according to claim 1 is characterized in that: Improved multi-agent operator construction: When the genetic operation update conditions do not meet the expected situation, the improved multi-agent catastrophic operation is performed, and binary search coding is used based on the stability of mutation and the feasibility of operation; This application uses the minimum fitness value to represent the global optimal fitness, sets a pregen-c as the stagnant generation, and performs multi-agent operation if the current generation is greater than the set generation, ensuring that the generation is between the second generation and the maximum generation, and the current optimal solution is less than the historical optimal solution, which is necessary to trigger the multi-agent operation. After the multi-agent operation, while retaining the optimal solution, set an arbitrary ratio to replace the original part, set a number rounding environment, and prevent the occurrence of decimal parts; After each multi-agent operation is completed, the new agent must be initialized, space is set to save the new fitness function, place is reserved to fill in numbers, a new objective function is defined, and the Griewank function is selected as the expression of the objective function. The new agents and their fitness after the disaster are arranged in descending order to facilitate the next genetic and multi-agent operations. Each multi-agent operation has a disaster mark for easy observation until the set maximum generation of genetic operations is reached. Starting from the overall update of the agent, the convergence and stability of the algorithm are taken into consideration.

10. The multi-agent modeling and state optimization method for hydro-generator control according to claim 1 is characterized in that: Regulator model construction: First, a model parameter initialization program is constructed to initialize the parameters of the regulator model and the turbine and water diversion system model. The model construction is started under SIMULINK. All the data recorded in the construction are stored in the file construction result file PC.mat in the form of a matrix. The variable name is Rdt. The data contained in this file is provided to the identification algorithm. The identification model is divided into two parts, namely, the controller model (controller + follow-up system) and the turbine and water diversion system model. The controller model includes PI-type controller and PID-type controller. The identification data used by the controller model are frequency disturbance and relay displacement, and the identification data used by the turbine and water diversion system model are relay displacement and turbine output torque. The corresponding ideal model of the controller is: A unified model discrimination criterion is introduced (all the following methods adopt this criterion), and the evaluation function value calculation method is: The main parameters of multi-agent are set as follows: agent size = 80; crossover probability = 0.7; mutation probability based on random traversal sampling strategy; maximum genetic generations = 100, and an optimal solution is retained in each generation of mutation.

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