A method for optimizing type II fuzzy control parameters of turbine regulation system

Abstract

The invention discloses a method for optimizing control parameters of a turbine regulating system based on type II fuzzy PID control, which is used for optimizing control of a hydropower unit. The control parameter optimization method is specifically as follows: first, a multi-operating-condition nonlinear simulation platform of a turbine regulating system is established, the adjustment range of the control parameters is preliminarily determined, a type I fuzzy controller is constructed, the domain of input and output is set according to the adjustment range of the control parameters, a membership function is set, and type I is converted into type II. Then, parameters such as the population size and evolutionary algebra of the improved PSO algorithm are set, and the population is initialized. Finally, according to the objective function and after a certain evolutionary algebra, the optimal PID control parameters can be obtained. The optimized PID controller can better guarantee the control performance of the turbine speed regulation system, and thus better meet the actual production needs of the power station.

Description

Technical Field

[0001] The invention relates to the technical field of water turbine regulating systems, and in particular to a method for optimizing type II fuzzy control parameters of a water turbine regulating system. Background Art

[0002] With the development of large-capacity high-head turbine units and long-distance ultra-high voltage transmission technology, in order to improve the power supply quality of the power grid, relevant scholars have put forward higher requirements for the regulation quality of hydropower units. The current turbine regulation control system widely adopts PID control rules. After the unit is connected to the grid, a set of PID control parameters is used to adjust the frequency and power of the unit. With the change of power load, the operating point of the turbine in actual operation will often change. Due to the continuous change of model parameters, it will be difficult to ensure the stable operation of the hydropower unit with a fixed PID regulation strategy, and it will not be able to meet the requirements for the control quality of the regulation system. At the same time, the turbine speed control system has the characteristics of nonlinearity, variable structure, and variable parameters, which makes it increasingly difficult for classical control theory to meet the requirements of higher regulation quality. The research on new control strategies based on modern control theory and intelligent control methods has become an inevitable trend of development.

[0003] The research on the optimization control of hydropower units mainly focuses on two aspects: one is to optimize the existing speed governor control parameters or structure to meet the needs of optimized operation of the unit; the other is to propose new control strategies to promote the development of hydropower unit speed governors. For the tuning and optimization of speed governor control parameters, the typical orthogonal method or gradient method has the disadvantages of being sensitive to the initial parameters and having high requirements for the differentiability of the objective function, which limits its application scope; the engineering tuning method is based on field tests and empirical formulas. The method is simple, but the optimal parameters obtained are often inaccurate. With the rise of artificial intelligence, heuristic algorithms and neural networks have begun to be widely used in the tuning and optimization of speed governor control parameters. These methods are based on the idea of ​​variable parameter PID and propose adaptive control laws. According to the current operating conditions of the turbine unit, intelligent algorithms are used to continuously change the current PID parameters. Compared with linear systems, the optimization of control parameters of nonlinear systems is more difficult because the mathematical equations of the control parameter optimization problem of nonlinear systems are more complex and there may be multiple local optimal solutions, which limits the application of traditional mathematical methods and requires higher solving capabilities of intelligent algorithms. In order to make the algorithm have excellent solution performance, many scholars have studied the improved algorithm based on heuristic algorithm and achieved ideal results. However, most of the improved algorithms are at the expense of higher calculation accuracy at a high computational cost, which inevitably introduces new parameters, increases the complexity of the algorithm itself, and greatly limits engineering applications. Among them, patent CN112012875A proposes a method for optimizing the PID control parameters of a turbine regulation system, but this method uses fuzzy control inertia weights and firefly-particle swarm hybrid evolution, which makes the algorithm's computational cost relatively high; the learning factor changes asynchronously, and the particle "self-learning" and "group learning" are carried out separately and mutually adjusted, and in some cases there is a defect of slow convergence speed. This patent is based on the above shortcomings and makes improvements to enable this method to achieve better control effects in a wider range of applications. Summary of the invention

[0004] The purpose of the present invention is to solve at least one of the technical problems existing in the prior art and to provide a method for optimizing PID control parameters of a turbine regulating system.

[0005] To achieve the above object, the technical solution adopted by the present invention is a method for optimizing control parameters of a turbine regulating system based on type-II fuzzy PID control, comprising the following steps:

[0006] Step 1: According to the working parameters, regulation coefficient and transfer function of the turbine, a nonlinear mathematical model of the turbine regulation system under multiple working conditions is established, and a simulation platform for collaborative optimization of PID parameters by using improved particle swarm algorithm and type-II fuzzy control is built in matlab / simulink;

[0007] Step 2: Build a fuzzy controller in Matlab, determine the domain and membership function of the output variable and input variable, and build the fuzzy controller according to certain fuzzy rules;

[0008] Step 3: Determine the uncertainty occupation space (FOU) of the MF, randomly configure the scaling factor of each MF, and convert the type-I fuzzy controller into a type-II fuzzy controller according to the corresponding bandwidth;

[0009] Step 4: Initialize and generate the original population and fuzzy particle swarm algorithm parameters;

[0010] Step 5: Substitute each candidate solution of the original population into the simulation platform for simulation, calculate the CostFunction indicator objective function value according to the error between the target signal and the response signal, and record the optimal solution of the local and global history of the original population. The calculation formula of the CostFunction indicator objective function value is as follows:

[0011]

[0012] Where t is the simulation time, e(t) is the error between the target signal and the response signal;

[0013] Step 6: Use the fuzzy particle swarm algorithm to iteratively calculate each particle of the original population, obtain the speed and position of all particles in the original population at the next moment, and use the population at the next moment as the new population. The formula for iterative calculation is as follows:

[0014]

[0015] Where: v i j (t+1) represents the velocity corresponding to the j-dimensional variable of the ith particle in the t+1th iteration, v i j (t) and x i j (t) represents the velocity and position of the j-dimensional variable of the ith particle in the t-th iteration, w is the inertia weight, c1 and c2 are learning factors, rand is a random number between 0 and 1, t is the number of iterations, p i j and g j They are the individual's historical optimal position and the group's historical optimal position respectively;

[0016]

[0017] Where: x i j (t+1) represents the position of the j-dimensional variable of the ith particle in the t+1th iteration, v ij (t) and x i j (t) represent the velocity and position corresponding to the j-dimensional variable of the ith particle in the tth iteration;

[0018] Among them, the inertia weight w of the adaptive adjustment algorithm is:

[0019]

[0020] Where: w min is the minimum inertia weight, w max is the maximum inertia weight, C min is the minimum value of CostFunction indicator, C avg is the average value of CostFunction indicator;

[0021] Learning factor c 1 , c 2 Synchronous changes:

[0022]

[0023] Where: c S , c L are the minimum and maximum values ​​of the learning factor, respectively. Generally, c S , c L Preset according to specific problems; t is the current number of iterations, T is the maximum number of iterations;

[0024] Step 7: Calculate the CostFunction indicator objective function value of the new population. If the CostFunction indicator objective function value of the new population is less than the optimal value of the individual history CostFunction indicator objective function of the original population, update it to the optimal value of the individual history CostFunction indicator objective function of the population. If the individual history CostFunction indicator objective function value of the new population is less than the optimal value of the group history CostFunction indicator objective function of the original population, update it to the optimal value of the group history CostFunction indicator objective function of the population, and record the optimal position of the individual population and the optimal solution of the global history.

[0025] Step 8: Determine whether the number of iterations of the current population has reached the maximum number of iterations. If so, terminate the iterative calculation and output the optimal position of the individual population and the optimal solution of the global history; otherwise, continue to iterate the current population.

[0026] Furthermore, in step 1, the turbine regulation system is divided into two parts: a speed governor and a regulation object. The speed governor adopts a type-II fuzzy PID control law, and the regulation object adopts a high-order generator model and a simplified power grid model.

[0027] Furthermore, in step 1, the simulation platform includes a controller, a servo system, a turbine and a water diversion system, a generator and a load, an excitation system and a power system stabilizer.

[0028] Furthermore, in step 5, the optimal solution is the fuzzy particle swarm algorithm parameter corresponding to the minimum objective function value of the CostFunction indicator.

[0029] Beneficial effects of the invention: From the above description of the invention, it can be seen that the present invention provides a method for optimizing the type II fuzzy control parameters of a turbine regulating system. Compared with the prior art, under a certain working condition of a hydraulic unit, the method utilizes a particle swarm algorithm with improved adaptive inertia weight and synchronous learning factor, and at the same time, a type II fuzzy control system is used to collaboratively optimize the PID control parameters, so that the CostFunction index of the system is minimized. The method can make the CostFunction index converge to the minimum value faster, greatly reduce the control adjustment time and overshoot, and have a certain robustness, thereby achieving the purpose of improving the regulation quality of the speed regulator, innovatively solving the problem that the control parameters of nonlinear systems are difficult to optimize, and reducing the workload of relevant engineering personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a design diagram of input variables of a type II fuzzy inference system for PID parameter optimization in a preferred embodiment of the present invention;

[0031] Figure 2 It is a design diagram of output variables and fuzzy rules of a type II fuzzy inference system for PID parameter optimization in a preferred embodiment of the present invention;

[0032] Figure 3 Schematic diagram of a multi-operating-condition simulation platform for a water turbine regulating system according to a preferred embodiment of the present invention;

[0033] Figure 4 A schematic diagram of the change of the objective function during the optimization process in an embodiment of the present invention;

[0034] Figure 5 Schematic diagram of the control effect of the optimal control parameters in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Reference Figure 1-5 As shown, the preferred embodiment of the present invention is a method for optimizing control parameters of a turbine regulating system based on type-II fuzzy PID control, comprising the following steps:

[0037] Step 1: According to the working parameters, regulation coefficient and transfer function of the turbine, a nonlinear mathematical model of the turbine regulation system under multiple working conditions is established, and a simulation platform for collaborative optimization of PID parameters by using improved particle swarm algorithm and type-II fuzzy control is built in matlab / simulink;

[0038] Step 2: Build a fuzzy controller in Matlab, determine the domain and membership function of the output variable and input variable, and build the fuzzy controller according to certain fuzzy rules;

[0039] Step 3: Determine the uncertainty occupation space (FOU) of the MF, randomly configure the scaling factor of each MF, and convert the type-I fuzzy controller into a type-II fuzzy controller according to the corresponding bandwidth;

[0040] Step 4: Initialize and generate the original population and fuzzy particle swarm algorithm parameters;

[0041] Step 5: Substitute each candidate solution of the original population into the simulation platform for simulation, calculate the CostFunction indicator objective function value according to the error between the target signal and the response signal, and record the optimal solution of the local and global history of the original population. The calculation formula of the CostFunction indicator objective function value is as follows:

[0042]

[0043] Where t is the simulation time, e(t) is the error between the target signal and the response signal;

[0044] Step 6: Use the fuzzy particle swarm algorithm to iteratively calculate each particle of the original population, obtain the speed and position of all particles in the original population at the next moment, and use the population at the next moment as the new population. The formula for iterative calculation is as follows:

[0045]

[0046] Where: v i j(t+1) represents the velocity corresponding to the j-dimensional variable of the ith particle in the t+1th iteration, v i j (t) and x i j (t) represents the velocity and position of the j-dimensional variable of the ith particle in the t-th iteration, w is the inertia weight, c1 and c2 are learning factors, rand is a random number between 0 and 1, t is the number of iterations, p i j and g j They are the individual's historical optimal position and the group's historical optimal position respectively;

[0047]

[0048] Where: x i j (t+1) represents the position of the j-dimensional variable of the ith particle in the t+1th iteration, v i j (t) and x i j (t) represent the velocity and position corresponding to the j-dimensional variable of the ith particle in the tth iteration;

[0049] Among them, the inertia weight w of the adaptive adjustment algorithm is:

[0050]

[0051] Where: w min is the minimum inertia weight, w max is the maximum inertia weight, C min is the minimum value of CostFunction indicator, C avg is the average value of CostFunction indicator;

[0052] Learning factor c 1 , c 2 Synchronous changes:

[0053]

[0054] Where: c S , c L are the minimum and maximum values ​​of the learning factor, respectively. Generally, c S , c L Preset according to specific problems; t is the current number of iterations, T is the maximum number of iterations;

[0055] Step 7: Calculate the CostFunction indicator objective function value of the new population. If the CostFunction indicator objective function value of the new population is less than the optimal value of the individual history CostFunction indicator objective function of the original population, update it to the optimal value of the individual history CostFunction indicator objective function of the population. If the individual history CostFunction indicator objective function value of the new population is less than the optimal value of the group history CostFunction indicator objective function of the original population, update it to the optimal value of the group history CostFunction indicator objective function of the population, and record the optimal position of the individual population and the optimal solution of the global history.

[0056] Step 8: Determine whether the number of iterations of the current population has reached the maximum number of iterations. If so, terminate the iterative calculation and output the optimal position of the individual population and the optimal solution of the global history; otherwise, continue to iterate the current population.

[0057] Table 1 Fuzzy rules table for Kp parameter adjustment

[0058]

[0059] Table 2 Fuzzy rules table for adjusting Ki parameters

[0060]

[0061] Table 3 Fuzzy rules table for Kd parameter adjustment

[0062]

[0063] In this embodiment, in step 1, the turbine regulation system is divided into two parts: a speed governor and a regulation object. The speed governor adopts a type II fuzzy PID control law, and the regulation object adopts a high-order generator model and a simplified power grid model. First, a type I fuzzy control system is established according to the fuzzy rules in the above table, and then the type I fuzzy controller is converted into a type II fuzzy controller according to the corresponding bandwidth, where the proportional factor of each MF is randomly configured.

[0064] In this embodiment, in step 1, the simulation platform includes a controller, a servo system, a turbine and a water diversion system, a generator and a load, an excitation system, and a power system stabilizer.

[0065] In this embodiment, in step 5, the optimal solution is the fuzzy particle swarm algorithm parameters corresponding to when the obtained CostFunction indicator objective function value is the minimum.

[0066] The present invention provides a method for optimizing type II fuzzy control parameters of a turbine regulating system. Compared with the prior art, under a certain working condition of a hydraulic unit, the method utilizes a particle swarm algorithm of improved adaptive inertia weight and synchronous learning factor, and at the same time, adopts a type II fuzzy control system to collaboratively optimize PID control parameters, so that the CostFunction index of the system is minimized. Compared with the method mentioned in patent CN112012875A, the method can make the CostFunction index converge to the minimum value faster, greatly reduce the control adjustment time and overshoot, and have a certain robustness, thereby having a higher governor adjustment quality, innovatively solving the problem that the control parameters of nonlinear systems are difficult to optimize, and further reducing the workload of relevant engineering personnel.

[0067] The above-mentioned embodiments only express several implementation methods of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that: without departing from the concept of the present invention, several modifications and improvements made should be within the scope of protection of the present invention.

Claims

1. A method for optimizing type II fuzzy control parameters of a turbine regulating system, characterized in that: The optimization method comprises the following steps: Step 1: Establish a nonlinear model of the turbine regulation system under multiple working conditions in matlab / simulink, and build a simulation platform for collaborative optimization of PID parameters using improved particle swarm optimization and type-II fuzzy control; Step 2: Build a type-I fuzzy controller in matlab, determine the domain and membership function of the output variable and input variable, and build the fuzzy controller according to certain fuzzy rules; determine the uncertainty occupied space FOU of MF, randomly configure the proportional factor of each MF, and convert the type-I fuzzy controller into a type-II fuzzy controller according to the corresponding bandwidth; Step 3: Generate the original population and initialize the parameters of the fuzzy particle swarm algorithm; substitute each candidate solution of the original population into the simulation platform for simulation, calculate the CostFunction indicator objective function value according to the error between the target signal and the response signal, and record the optimal solution of the local and global history of the original population. The calculation formula of the CostFunction indicator objective function value is as follows: Where t is the simulation time, e(t) is the error between the target signal and the response signal; Step 4: Use the fuzzy particle swarm algorithm to iteratively calculate each particle of the original population, obtain the speed and position of all particles in the original population at the next moment, and use the population at the next moment as the new population. The formula for iterative calculation is as follows: Where: v i j (t+1) represents the velocity corresponding to the j-dimensional variable of the ith particle in the t+1th iteration, v i j (t) and x i j (t) represents the velocity and position of the j-dimensional variable of the ith particle in the t-th iteration, w is the inertia weight, c1 and c2 are learning factors, rand is a random number between 0 and 1, t is the number of iterations, p i j and g j They are the individual's historical optimal position and the group's historical optimal position respectively; Where: x i j (t+1) represents the position of the j-dimensional variable of the ith particle in the t+1th iteration, v i j (t) and x i j (t) represent the velocity and position corresponding to the j-dimensional variable of the ith particle in the tth iteration; Step 5: Calculate the CostFunction indicator objective function value of the new population. If the CostFunction indicator objective function value of the new population is less than the optimal value of the individual historical CostFunction indicator objective function of the original population, update it to the optimal value of the individual historical CostFunction indicator objective function of the population. If the individual historical CostFunction indicator objective function value of the new population is less than the optimal value of the group historical CostFunction indicator objective function of the original population, update it to the optimal value of the group historical CostFunction indicator objective function of the population, and record the optimal position of the individual population and the optimal solution of the global history; determine whether the number of iterations of the current population has reached the maximum number of iterations. If it has reached the maximum number of iterations, terminate the iterative calculation and output the optimal position of the individual population and the optimal solution of the global history; otherwise, continue to iterate the current population.

2. The method for optimizing type II fuzzy control parameters of a turbine regulating system according to claim 1, characterized in that: In step 4, the optimal solution is the fuzzy particle swarm algorithm parameter corresponding to the minimum CostFunction indicator objective function value; Among them, the inertia weight w of the adaptive adjustment algorithm is: Where: w min is the minimum inertia weight, w max is the maximum inertia weight, C min is the minimum value of CostFunction indicator, C avg is the average value of CostFunction indicator; Learning factors c1, c2 change synchronously: Where: c S , c L are the minimum and maximum values ​​of the learning factor, c S , c L Preset according to the specific problem; t is the current number of iterations, and T is the maximum number of iterations.

3. The method for optimizing type II fuzzy control parameters of a turbine regulating system according to claim 1, characterized in that: In step 2, the turbine regulation system is divided into two parts: a speed governor and a regulation object. The speed governor adopts a type-II fuzzy PID control law, and the regulation object adopts a high-order generator model and a simplified power grid model.

4. The method for optimizing type II fuzzy control parameters of a turbine regulating system according to claim 1, characterized in that: In step 1, the simulation platform includes a controller, a servo system, a turbine and a water diversion system, a generator and a load, an excitation system and a power system stabilizer.

Images