Method for reducing short circuit current of doubly-fed wind power generator set

Abstract

The application discloses a method for reducing short-circuit current of a double-fed wind turbine generator unit based on an improved lion swarm optimization algorithm, optimizes a rotor side control strategy of a double-fed wind power generation system, and better limits large current generated by the rotor side of the double-fed wind power generation system when a short-circuit fault occurs in a power grid under the environment of high proportion of renewable energy and high proportion of power electronic equipment (referred to as "double high") in a modern new power system. The specific steps are as follows: Step 1: constructing a double-fed wind power generation system model; Step 2: improving the lion swarm optimization algorithm, including two improvements: D1, using an automatic parameter adjusting step to improve the search space of the lion swarm optimization algorithm, and D2, introducing a new population updating strategy to solve the local optimal problem of the lion swarm optimization algorithm, named "pathfinding knight population"; Step 3: using the improved lion swarm optimization algorithm to optimize parameter setting of a PID controller of the double-fed wind power generation system, obtaining two parameters Ki and Kp of the optimized PID controller through a certain number of iterations; and Step 4: simulating the PID control system of the double-fed wind power generation system by using MATLAB and Simiulink, so that the improved lion swarm algorithm has the ability to obtain a global optimal solution and convergence speed, has faster global optimization speed and precision, can obtain the optimal result faster, and is more robust in solving the local optimal problem.

Description

Technical Field

[0001] The present invention belongs to the field of new energy grid-connected power generation control, and in particular relates to a method for reducing the short-circuit current of a doubly-fed wind turbine generator set. Background Art

[0002] With the large-scale integration of renewable energy sources and the widespread commissioning of power electronic devices, modern power systems are characterized by a high proportion of both renewable energy and power electronic devices (referred to as "double highs"). The increasing proportion of renewable energy generation has led to increasingly complex grid structures, making power flows and the evolution of grid faults unpredictable, posing new challenges to the safe and stable operation of power systems. Power system fault analysis is fundamental to the study of safe and stable power system operation, and reducing short-circuit currents in power systems, thereby minimizing the impact on new power systems, is particularly important.

[0003] When a doubly-fed wind turbine is connected to the grid, the generator is directly connected to the grid through its stator, while the rotor is connected to the grid via a back-to-back bidirectional converter. When a short-circuit fault occurs in the grid, a high current flows on the stator side. Due to electromagnetic coupling between the stator and rotor and the law of flux conservation, the fault on the stator side is transmitted to the rotor side, causing a high current to flow there as well. To prevent high currents from damaging the converter, it is necessary to optimize the converter control strategy to control the short-circuit current in the doubly-fed wind turbine. Summary of the Invention

[0004] The present invention aims to propose a method for reducing the short-circuit current of a doubly-fed wind turbine generator set. This method solves the problem of the Lion Group Optimization algorithm's slow convergence speed and tendency to fall into local optimality, thus preventing it from finding a better solution. The method enhances the algorithm's stability and performance. Furthermore, the improved Lion Group Optimization algorithm is used to optimize PID control parameters, resolving the problem of poor short-circuit current control due to insufficient PID parameters. This method can effectively improve the control performance and stability of the system.

[0005] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0006] A method for reducing the short-circuit current of a doubly-fed wind turbine generator set uses an improved lion group optimization algorithm to optimize the parameters of the PID controller. The specific steps are as follows:

[0007] Step 1: Construct a doubly-fed wind power generation system model;

[0008] Step 2: Improve the Lion Group Optimization Algorithm. Use an automatic parameter adjustment step to improve the search space of the Lion Group Optimization Algorithm. First adjust the integer part and then the decimal part. Each time it is updated, the given range of Kp and Ki is divided into three equal parts. Then, a Kp and a Ki are randomly generated within these three ranges to generate 9 individuals.

[0009] Step three: using improved lion optimization algorithm to optimize the parameter setting of the PID controller of the doubly-fed wind power system, through a certain number of iterations, the optimized Ki and Kp parameters of the PID controller are obtained, which specifically includes:

[0010] S1, algorithm parameter initialization: set the upper and lower limits of Kp and Ki searched by the lion optimization algorithm, the population size popsize, the decimal precision floatPrecision, and the maximum iteration number epochCountDown;

[0011] S2, the parameter value range of the PID control system of the doubly-fed wind power system is used as the search space of the lion search agent group, and a set of PID control system parameters are randomly selected within the value range as the initial position of the search agent group;

[0012] S3, an optimization objective function is constructed around the dq-axis current in the rotor-side converter for calculating the fitness value, and the formula of the objective function is object=sum(idr^2)+sum(iqr^2)

[0013] In the formula, object is the objective function, idr is the d-axis current of the rotor-side converter, and iqr is the q-axis current of the rotor-side converter.

[0014] S4, calculate the current fitness value of each individual of the improved lion optimization algorithm, select the minimum fitness value of the current iteration as the optimal fitness value, and select the maximum fitness value of the current iteration as the worst fitness value;

[0015] S5, assign the optimal solution to Kp and Ki respectively, and pass the optimized parameters to the PID controller to complete the optimization of the PI controller parameters;

[0016] Step four: use MATLAB and Simulink to simulate the PID control system of the doubly-fed wind power system.

[0017] Further, in step one, the calculation module of target value and real-time value, the improved lion optimization algorithm module, and the controlled object module are included.

[0018] Further, in step two, D1, an automatic parameter tuning step is used to improve the search space of the lion optimization algorithm, which first tunes the integer part and then tunes the decimal part. This step not only reduces the parameter search range, but also reduces the search difficulty of the algorithm and improves the efficiency of parameter optimization. For the optimization of the decimal part, upward decimal search and downward decimal search strategies are used to find better parameters within the range of integer plus or minus 1. This method effectively expands the search space and increases the possibility of finding the optimal solution by the algorithm.

[0019] Further, in the step two, D2, in order to deal with the problem that the traditional genetic algorithm is easy to fall into local optimum, a new population updating strategy is introduced, called "pathfinder knight population". Each time of updating, the given range of Kp and Ki is divided into three equal parts, and a Kp and a Ki are randomly generated in the three ranges, so as to generate 9 individuals. Such population structure can more effectively explore the solution space and slow down the problem of local optimum.

[0020] Further, in the step three, when performing gene crossing operation, all population individuals can only obtain the gene fragments of the historical optimal individual in the population. Such design makes the genetic algorithm more inclined to retain the characteristics of excellent individuals, thereby helping to improve the convergence speed of the algorithm. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 To improve the lion optimization algorithm for controlling the short-circuit current of a doubly-fed wind turbine generator set

[0022] Figure 2 For the control block diagram of the rotor side converter

[0023] Figure 3 For the comparison curve of the fitness function variation of the original lion algorithm and the improved lion optimization algorithm

[0024] Figure 4 For the comparison chart of the effect of the original lion algorithm and the improved lion optimization algorithm for controlling the short-circuit current of a doubly-fed wind turbine generator set DETAILED DESCRIPTION

[0025] The embodiments of the present application will be described in detail below with specific reference to the drawings. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in the specification. The present application can also be implemented or applied in other different embodiments, and the details in the specification can be modified or changed in various ways based on different views and applications without departing from the spirit of the present application. It should be noted that the following examples and features in the examples can be combined with each other without conflict.

[0026] Please refer to Figures 1-4 The present application provides a technical solution:

[0027] A method for reducing the short-circuit current of a doubly-fed wind turbine generator set, which optimizes the parameters of a PID controller using an improved lion optimization algorithm, the specific steps being as follows:

[0028] Step 1: Construct a doubly-fed wind power system model

[0029] Step two: improve the lion optimization algorithm, use an automatic parameter adjustment step to improve the search space of the lion optimization algorithm, first set the integer part and then set the decimal part, each time update, divide the given range of Kp and Ki into three equal parts, and randomly generate a Kp and a Ki in the three parts, to generate 9 individuals;

[0030] Step three: use the improved lion optimization algorithm to optimize the parameter setting of the PID controller of the double-fed wind power system, and through a certain number of iterations, obtain the optimized Ki and Kp parameters of the PID controller, which specifically includes:

[0031] S1, algorithm parameter initialization: set the upper and lower limits of Kp and Ki searched by the lion optimization algorithm, the population size popsize, the decimal precision floatPrecision, and the maximum iteration number epochCountDown;

[0032] S2, the parameter value range of the PID control system of the double-fed wind power system is used as the search space of the lion search agent group, and a set of PID control system parameters are randomly selected in the value range as the initial position of the search agent group;

[0033] S3, an optimization objective function is constructed around the dq-axis current in the rotor-side converter for calculating the fitness value, and the formula of the objective function is object=sum(idr^2)+sum(iqr^2)

[0034] In the formula, object is the objective function, idr is the d-axis current of the rotor-side converter, and iqr is the q-axis current of the rotor-side converter.

[0035] S4, calculate the current fitness value of each individual of the improved lion optimization algorithm, select the minimum fitness value of the current iteration as the optimal fitness value, and select the maximum fitness value of the current iteration as the worst fitness value.

[0036] S5, assign the optimal solution of the search to Kp and Ki respectively, and pass the optimized parameters to the PID controller to complete the optimization of the PI controller parameters.

[0037] Step four: use MATLAB and Simulink to simulate the PID control system of the double-fed wind power system.

[0038] Further, in step one, the calculation module of the target value and the real-time value, the improved lion optimization algorithm module, and the controlled object module are included.

[0039] Further, in step two, D1, an automatic parameter adjustment step is used to improve the search space of the lion swarm optimization algorithm. The integer part is adjusted first, and then the decimal part. This step not only reduces the search range of the parameters, but also reduces the search difficulty of the algorithm and improves the efficiency of parameter optimization. For the optimization of the decimal part, upward and downward decimal search strategies are used to find better parameters within the range of integer plus or minus 1. This method effectively expands the search space and increases the possibility of finding the optimal solution.

[0040] Further, in step two, D2, to address the problem of traditional genetic algorithms easily falling into local optima, a new population update strategy called "pathfinder knight population" is introduced. Each time the population is updated, the given range of Kp and Ki is divided into three equal parts, and a Kp and a Ki are randomly generated within these three ranges to generate nine individuals. Such a population structure can more effectively explore the solution space and slow down the problem of local optimality.

[0041] Further, in step three, when performing gene crossover operation, all population individuals can only obtain the gene fragments of the historical optimal individual in the population. Such design makes the genetic algorithm more inclined to retain the characteristics of excellent individuals, thereby helping to improve the convergence speed of the algorithm.

Claims

1. A method for reducing short-circuit current of a doubly-fed wind turbine generator set, characterized in that: The improved lion group optimization algorithm is used to optimize the parameters of the PID controller. The specific steps are as follows: Step 1: Construct a doubly-fed wind power generation system model; Step 2: Improve the Lion Group Optimization Algorithm. Use an automatic parameter adjustment step to improve the search space of the Lion Group Optimization Algorithm. First adjust the integer part and then the decimal part. Each time it is updated, the given range of Kp and Ki is divided into three equal parts. Then, a Kp and a Ki are randomly generated within these three ranges to generate 9 individuals. Step 3: Use the improved lion group optimization algorithm to optimize the parameters of the PID controller of the doubly fed wind power generation system. After a certain number of iterations, the two parameters Ki and Kp of the optimized PID controller are obtained, including: S1. Initialize the algorithm parameters: set the upper and lower limits of Kp and Ki, the population size popsize, the decimal precision floatPrecision, and the maximum number of iterations epochCountDown for the lion group optimization algorithm search; S2. The parameter value range of the PID control system of the doubly-fed wind power generation system is used as the search space of the lion group search agent group, and a set of PID control system parameters are randomly selected within the value range as the initial position of the search agent group; S3. Construct an optimization objective function around the dq axis current in the rotor-side converter to calculate the fitness value. The objective function formula is: object=sum(idr^2)+sum(idq^2) Where, object is the objective function, idr is the d-axis current of the rotor-side converter, and iqr is the q-axis current of the rotor-side converter; S4. Calculate the current fitness value of each individual in the improved lion group algorithm, select the fitness value with the minimum in the current iteration as the optimal fitness value, and select the fitness value with the maximum in the current iteration as the worst fitness value; S5. Assign the searched optimal solution to Kp and Ki respectively, and pass the optimized parameters to the PID controller to complete the optimization of the PI controller parameters; Step 4: Use MATLAB and Simulink to simulate the PID control system of the doubly fed wind power generation system.

2. A method for reducing short-circuit current of a doubly-fed wind turbine generator set according to claim 1, characterized in that: In step three, when performing the gene crossover operation, all individuals in the population can only obtain the gene fragments of the historical best individual in the population.

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