Wind power plant reactive power optimization method and device based on genetic algorithm

Abstract

The invention provides a wind power plant reactive power optimization method and device based on a genetic algorithm, and the method comprises the steps: building a reactive power optimization mathematical model of the network loss of a doubly-fed wind power plant current collection system based on the active loss of a doubly-fed wind power plant power transmission line and a transformer, according to the reactive power optimization mathematical model of the grid loss of the doubly-fed wind power plant current collection system, constructing a target function of the grid loss of the doubly-fed wind power plant current collection system; the method comprises the following steps of: constructing a constraint condition of network loss of a doubly-fed wind power plant current collection system according to reactive power requirements of fan reactive power output limit, node voltage, branch apparent power and grid-connected point voltage stability of the doubly-fed wind power plant, processing the constraint condition by using a penalty function, and obtaining a constraint-containing target function according to the target function and the processed constraint condition; and on the basis of the constraint-containing objective function, optimizing and calculating a reactive power output reference value of the doubly-fed fan by adopting a genetic algorithm. The method can reduce the network loss of the current collection system in the wind power plant.

Description

Technical Field

[0001] The present application relates to the technical field of wind farm reactive power optimization, and in particular to a method and device for wind farm reactive power optimization based on a genetic algorithm. Background Art

[0002] Wind power generation is one of the main energy sources for building a new power system. In order to control the voltage at the wind farm grid connection point within the allowable range of the power grid and maintain the stable operation of the power grid, the current domestic power system adopts an automatic voltage control system (Automatic Voltage Control, AVC). The reactive power setting value sent by the power grid to the wind farm grid connection point under the AVC master station must meet the supply and demand balance between the power grid and the wind farm, and also consider the power loss of the collection system. However, the network loss of the collection system in the existing wind farm (active power loss in the transmission line and transformer) is relatively large. Summary of the invention

[0003] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0004] To this end, the first purpose of this application is to propose a wind farm reactive power optimization method based on genetic algorithm, which solves the technical problem of large network losses in the collection system of existing wind farms (active power losses in transmission lines and transformers). By establishing a reactive optimization mathematical model of the network losses of the wind farm collection system and then using a genetic algorithm to optimize the calculation of the reactive output reference value of the doubly fed wind turbine, the network losses of the collection system in the wind farm are effectively reduced.

[0005] The second objective of the present application is to propose a wind farm reactive power optimization device based on a genetic algorithm.

[0006] The third objective of the present application is to provide a computer device.

[0007] A fourth object of the present application is to provide a non-transitory computer-readable storage medium.

[0008] To achieve the above-mentioned objectives, the first embodiment of the present application proposes a method for optimizing reactive power of a wind farm based on a genetic algorithm, including: establishing a reactive optimization mathematical model for the network loss of a doubly-fed wind farm collection system based on the active power loss of the doubly-fed wind farm transmission lines and transformers, and constructing an objective function for the network loss of a doubly-fed wind farm collection system based on the reactive optimization mathematical model for the network loss of a doubly-fed wind farm collection system; constructing constraints for the network loss of a doubly-fed wind farm collection system based on the reactive output limit of the wind turbine in the doubly-fed wind farm, the node voltage, the apparent power of the branch, and the reactive power demand for voltage stability at the grid connection point, and using a penalty function to process the constraints, and obtaining a constrained objective function based on the objective function and the processed constraints; and using a genetic algorithm to optimize the calculation of a reference value for the reactive output of the doubly-fed wind turbine based on the constrained objective function.

[0009] Optionally, in one embodiment of the present application, the reactive power optimization mathematical model of the network loss of the doubly-fed wind farm collection system is expressed as:

[0010]

[0011] in, and Represents the active power loss of each transformer and each cable, N trans Indicates the number of transformers, N L Represents the transmission line sub-index.

[0012] Optionally, in one embodiment of the present application, the objective function of the network loss of the doubly-fed wind farm collection system is expressed as:

[0013]

[0014] Among them, P loss represents the network loss of the wind farm collection system, P WTi represents the active output of the i-th wind turbine, P PCC It indicates the measured active value of the high-voltage busbar of the main transformer of the wind farm.

[0015] Optionally, in one embodiment of the present application, according to the reactive power output limit of the wind turbine of the doubly-fed wind farm, the node voltage, the branch apparent power, and the reactive power demand for the voltage stability of the grid connection point, the constraint conditions of the network loss of the doubly-fed wind farm collection system are constructed, and the constraint conditions are processed using a penalty function, including:

[0016] According to the reactive power output limit of the wind turbine in the doubly-fed wind farm, the node voltage, the apparent power of the branch, and the reactive power demand for the voltage stability at the grid connection point, the wind turbine reactive output constraint conditions, the node voltage constraint conditions, the branch apparent power constraint conditions, and the reactive power demand constraint conditions for the network loss of the doubly-fed wind farm collection system are constructed;

[0017] The penalty function is used to process the wind turbine reactive output constraints, node voltage constraints, and branch apparent power constraints, and after the reactive demand constraints are converted into inequality constraints, the penalty function is used to process the converted constraints to obtain the processed constraints; among them,

[0018] The wind turbine reactive output constraints, node voltage constraints, branch apparent power constraints and reactive demand constraints are expressed as:

[0019]

[0020]

[0021]

[0022]

[0023] Among them, Q WTi Represents the reactive power output of each fan, U j represents the voltage of the jth node, Sj represents the apparent power of each j-branch, N bus Indicates the number of buses, N WT Indicates the total number of fans;

[0024] The processed constraints are expressed as:

[0025]

[0026] Among them, w1, w2 and w3 represent the weights of the penalty function, N bus Indicates the number of buses, N WT Indicates the total number of fans.

[0027] Optionally, in one embodiment of the present application, the objective function with constraints is expressed as:

[0028]

[0029] Among them, f is the penalty function, the expression is:

[0030]

[0031] P losses represents the objective function, w1, w2 and w3 represent the weights of the penalty function, v represents the reactive power reference value of the wind turbines participating in the dispatch, u represents the node voltage, Indicates the reactive power measurement value of the grid connection point.

[0032] Optionally, in one embodiment of the present application, based on a constrained objective function, a genetic algorithm is used to optimize and calculate a reactive output reference value of a doubly-fed wind turbine, including:

[0033] Initialize the control parameters of the genetic algorithm and randomly generate an initial population with a fixed length, where each individual in the population represents a candidate solution;

[0034] The objective function with constraints is used as the fitness function, and the fitness of each individual in the population is calculated according to the active power loss of the wind farm and the reactive power demand of the PCC point;

[0035] According to the fitness, it is judged whether the corresponding individual meets the preset optimization standard. If not, the population with the highest fitness is copied based on probability and random methods, and crossover and mutation operations are performed to generate a new population. The fitness of each individual in the new population is calculated until the preset optimization standard is met, and the reactive output reference value of the doubly fed wind turbine is obtained.

[0036] Optionally, in one embodiment of the present application, initializing the control parameters of the genetic algorithm includes:

[0037] Set the population size, crossover rate, mutation rate and maximum number of iterations based on experimental experience.

[0038] To achieve the above-mentioned purpose, the second embodiment of the present application proposes a wind farm reactive power optimization device based on a genetic algorithm, comprising:

[0039] The first construction module is used to establish a reactive power optimization mathematical model of the network loss of the doubly-fed wind farm collection system based on the active power loss of the doubly-fed wind farm transmission line and transformer, and to construct an objective function of the network loss of the doubly-fed wind farm collection system according to the reactive power optimization mathematical model of the network loss of the doubly-fed wind farm collection system;

[0040] The second construction module is used to construct the constraint conditions of the network loss of the doubly-fed wind farm collection system according to the reactive output limit of the wind turbine of the doubly-fed wind farm, the node voltage, the branch apparent power, and the reactive demand of the grid connection point voltage stability, and use the penalty function to process the constraint conditions, and obtain the constrained objective function according to the objective function and the processed constraint conditions;

[0041] The optimization module is used to optimize and calculate the reactive power reference value of the doubly-fed wind turbine based on a constrained objective function using a genetic algorithm.

[0042] To achieve the above objectives, the third aspect of the present application proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the wind farm reactive power optimization method based on genetic algorithm described in the above embodiment is implemented.

[0043] In order to achieve the above-mentioned objectives, the fourth aspect of the present application proposes a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by a processor, a wind farm reactive power optimization method based on a genetic algorithm can be executed.

[0044] The wind farm reactive power optimization method, device, computer equipment and non-temporary computer-readable storage medium based on genetic algorithm in the embodiments of the present application solve the technical problem of large network loss of the collection system in the existing wind farm (active power loss in the transmission line and transformer). By establishing a reactive optimization mathematical model of the network loss of the wind farm collection system and then using a genetic algorithm to optimize the calculation of the reactive output reference value of the doubly fed wind turbine, the network loss of the collection system in the wind farm is effectively reduced.

[0045] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0047] Figure 1 A schematic diagram of a process flow of a wind farm reactive power optimization method based on a genetic algorithm provided in Embodiment 1 of the present application;

[0048] Figure 2 A basic structural diagram of a genetic algorithm executing an optimization process of a wind farm reactive power optimization method based on a genetic algorithm according to an embodiment of the present application;

[0049] Figure 3 A schematic diagram of the structure of a wind farm reactive power optimization device based on a genetic algorithm provided in Example 2 of the present application. DETAILED DESCRIPTION

[0050] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0051] Genetic Algorithm (GA) is a random search method based on the metaphorical mechanism of natural biological evolution. Genetic algorithms operate on the basis of potential solutions and use the principle of survival of the fittest to obtain better approximate solutions that are closer to the optimal solution. In each generation of genetic operations, individuals are selected according to their fitness in the problem domain to create a new and better set of approximate solutions. This process allows each individual in the population to inherit the excellent characteristics of the previous generation of individuals and continuously produce new individuals that are more suitable for the environment. The characteristic of this method is that it directly performs genetic operations similar to biological evolution on the target object of the research problem, without limiting the continuity of the function and high-order differential derivative problems. The algorithm regards each influencing variable of the problem under study as an independent individual in the biological population, performs encoding operations on each individual in the initial population, simulates the phenomenon of survival of the fittest in the process of genetic evolution, and repeatedly performs gene selection, crossover and mutation operations between individuals in the population. In this evolutionary process, each individual evaluates each new individual according to the fitness function, continuously eliminates individuals with poor fitness, and searches for individuals with the largest fitness to obtain the optimal solution that meets the problem.

[0052] The following describes a method and device for optimizing reactive power of a wind farm based on a genetic algorithm according to an embodiment of the present application with reference to the accompanying drawings.

[0053] Figure 1 A schematic flow chart of a method for optimizing reactive power of a wind farm based on a genetic algorithm provided in Embodiment 1 of the present application.

[0054] like Figure 1 As shown, the wind farm reactive power optimization method based on genetic algorithm includes the following steps:

[0055] Step 101, based on the active power loss of the transmission line and transformer of the doubly-fed wind farm, a reactive power optimization mathematical model of the network loss of the doubly-fed wind farm collection system is established, and according to the reactive power optimization mathematical model of the network loss of the doubly-fed wind farm collection system, an objective function of the network loss of the doubly-fed wind farm collection system is constructed;

[0056] Step 102, constructing the constraint conditions of the network loss of the doubly-fed wind farm collection system according to the reactive output limit of the wind turbine of the doubly-fed wind farm, the node voltage, the branch apparent power, and the reactive demand of the grid connection point voltage stability, and using the penalty function to process the constraint conditions, and obtaining the constrained objective function according to the objective function and the processed constraint conditions;

[0057] Step 103, based on the constrained objective function, a genetic algorithm is used to optimize and calculate the reactive power reference value of the doubly-fed wind turbine.

[0058] The method for optimizing reactive power of a wind farm based on a genetic algorithm in an embodiment of the present application establishes a reactive optimization mathematical model for network losses of a doubly-fed wind farm collection system based on the active power losses of a doubly-fed wind farm transmission line and a transformer, and constructs an objective function for network losses of a doubly-fed wind farm collection system based on the reactive optimization mathematical model for network losses of a doubly-fed wind farm collection system; constrains for network losses of a doubly-fed wind farm collection system are constructed based on reactive power output limits of wind turbines in the doubly-fed wind farm, node voltage, branch apparent power, and reactive power requirements for voltage stability at the grid connection point, and a penalty function is used to process the constraints, and a constrained objective function is obtained based on the objective function and the processed constraints; based on the constrained objective function, a genetic algorithm is used to optimize and calculate a reference value for reactive output of a doubly-fed wind turbine. In this way, the technical problem of large network losses in the existing wind farm collection system (active power losses in transmission lines and transformers) can be solved. By establishing a reactive optimization mathematical model for the network losses of the wind farm collection system and then using a genetic algorithm to optimize the calculation of the reactive output reference value of the doubly fed wind turbine, the network losses of the wind farm collection system can be effectively reduced.

[0059] The optimal reactive power dispatch strategy proposed in this application aims to minimize the network loss of the wind farm collection system, including the active power loss in the transmission line and transformer. The reactive power output reference value of the doubly-fed wind turbine is optimized and calculated by using a genetic algorithm by establishing a reactive power optimization mathematical model.

[0060] Furthermore, in the embodiment of the present application, the reactive power optimization mathematical model of the network loss of the doubly-fed wind farm collection system is expressed as:

[0061]

[0062] in, and Represents the active power loss of each transformer and each cable, N trans Indicates the number of transformers, N L Represents the transmission line sub-index.

[0063] Furthermore, in the embodiment of the present application, the objective function of the network loss of the doubly-fed wind farm collection system is expressed as:

[0064]

[0065] Among them, P loss represents the network loss of the wind farm collection system, P WTi represents the active output of the i-th wind turbine, P PCc It indicates the measured active value of the high-voltage busbar of the main transformer of the wind farm.

[0066] This application establishes a doubly-fed wind farm model on Matlab / Simulink simulation. Since the voltage, active / reactive power and other variables of all nodes can be measured, the most efficient reactive power dispatching strategy based on the minimum network loss of the wind farm collection system (transformer and transmission line) can be expressed as:

[0067]

[0068] Among them, F loss represents the network loss of the wind farm collection system, PWTi represents the active output of the i-th wind turbine, It indicates the measured active value of the high-voltage busbar of the main transformer of the wind farm.

[0069] Further, in the embodiment of the present application, according to the reactive power output limit of the wind turbine of the doubly-fed wind farm, the node voltage, the branch apparent power, and the reactive power demand for the voltage stability of the grid connection point, the constraint conditions of the network loss of the doubly-fed wind farm collection system are constructed, and the constraint conditions are processed using a penalty function, including:

[0070] According to the reactive power output limit of the wind turbine in the doubly-fed wind farm, the node voltage, the apparent power of the branch, and the reactive power demand for the voltage stability at the grid connection point, the wind turbine reactive output constraint conditions, the node voltage constraint conditions, the branch apparent power constraint conditions, and the reactive power demand constraint conditions for the network loss of the doubly-fed wind farm collection system are constructed;

[0071] The penalty function is used to process the wind turbine reactive output constraints, node voltage constraints, and branch apparent power constraints, and after the reactive demand constraints are converted into inequality constraints, the penalty function is used to process the converted constraints to obtain the processed constraints; among them,

[0072] The wind turbine reactive output constraints, node voltage constraints, branch apparent power constraints and reactive demand constraints are expressed as:

[0073]

[0074]

[0075]

[0076]

[0077] Among them, Q WTi Represents the reactive power output of each fan, U j represents the voltage of the jth node, S j represents the apparent power of each j branch, N bus Indicates the number of buses, N WT Indicates the total number of fans;

[0078] The processed constraints are expressed as:

[0079]

[0080] Among them, W1, w2 and w3 represent the weights of the penalty function, Nb u s represents the number of buses, N WT Indicates the total number of fans.

[0081] The network loss of the wind farm collection system is a function of a series of control variables and independent variables, including the reactive dispatch reference value of each doubly fed wind turbine, the voltage and reactive power of the wind farm node, and the reactive output limit of the wind turbine. These variables constitute the constraints of the optimization problem, which are specifically expressed as:

[0082]

[0083]

[0084]

[0085] In addition, there is a special equality constraint. To meet the reactive power demand for grid voltage stability It is often used as a constraint condition for reactive power optimization problems and is expressed as:

[0086]

[0087] Among them, Q WTi Represents the reactive power output of each fan, U j represents the voltage of the jth node, S j represents the apparent power of each j branch, N bus Indicates the number of buses, N WT Indicates the total number of wind turbines. In order to maintain the node voltage within the allowable range, the node voltage amplitude is uniformly set to 0.95pu-1.05pu.

[0088] Constraint processing plays an important role in optimization search. Choosing the right method to handle these constraints is also crucial to the convergence speed of the optimization solution. This application uses a penalty function to solve optimization problems with constraints. The principle of the penalty function is that if a variable violates an inequality constraint, it will be penalized, and then the penalized constraint will be extended to the original objective function. Therefore, the penalty function for inequality constraints such as node voltage, wind turbine reactive output limit, and wind turbine reactive dispatch reference value in this application is as follows:

[0089]

[0090]

[0091] Among them, v represents the control variable (independent variable), which refers to the reactive power reference value of the wind turbine involved in the dispatch; u represents the state variable (dependent variable), including the node voltage and the reactive power demand range of the PCC node; w1, w2 and w3 refer to the weights of the penalty function, which generally increase with the number of iterations and have little effect on the number of iterations. Although the above method can solve these inequality constraint problems well, the equality constraint still needs to be solved. Since the reactive power loss of the transformer is the main factor affecting the reactive power flow, in order to search for the optimal solution as soon as possible, the equality constraint can be appropriately modified and appropriate deviations are allowed as follows:

[0092]

[0093] In this way, the equality constraint is transformed into the inequality constraint, which can also be solved by using the penalty function. Therefore, the penalty function for handling the constraint in this application can be given by the following formula:

[0094]

[0095] In summary, when a variable exceeds the constraint range, the penalty function for all variables is handled as follows:

[0096]

[0097] Among them, w1, w2 and w3 represent the weights of the penalty function, N bus Indicates the number of buses, N WT Indicates the total number of fans.

[0098] Furthermore, in the embodiment of the present application, the objective function with constraints is expressed as:

[0099]

[0100] Among them, f is the penalty function, the expression is:

[0101]

[0102] P losses represents the objective function, w1, w2 and w3 represent the weights of the penalty function, v represents the reactive power reference value of the wind turbines participating in the dispatch, u represents the node voltage, Indicates the reactive power measurement value of the grid connection point, N bus Indicates the number of buses, N WT Indicates the total number of fans.

[0103] Combining the objective function and constraints, the constrained objective function established after using the penalty function is as follows:

[0104]

[0105] Among them, P losses represents the objective function, w1, w2 and w3 represent the weights of the penalty function, N bus Indicates the number of buses, N WT Indicates the total number of fans.

[0106] Further, in the embodiment of the present application, based on the objective function with constraints, a genetic algorithm is used to optimize and calculate the reactive output reference value of the doubly-fed wind turbine, including:

[0107] Initialize the control parameters of the genetic algorithm and randomly generate an initial population with a fixed length, where each individual in the population represents a candidate solution;

[0108] The objective function with constraints is used as the fitness function, and the fitness of each individual in the population is calculated according to the active power loss of the wind farm and the reactive power demand of the PCC point;

[0109] According to the fitness, it is judged whether the corresponding individual meets the preset optimization standard. If not, the population with the highest fitness is copied based on probability and random methods, and crossover and mutation operations are performed to generate a new population. The fitness of each individual in the new population is calculated until the preset optimization standard is met, and the reactive output reference value of the doubly fed wind turbine is obtained.

[0110] Figure 2 The basic structure of a simple genetic algorithm to perform the optimization process is shown. Figure 2 As shown, at the beginning of the optimization process, some individuals (populations) are randomly initialized, including population size, number of evolutions, crossover and mutation probabilities. Each individual (string) represents a feasible solution, and then the fitness of these individuals is evaluated according to the fitness function (objective function). If the optimal string (or string group) meets the optimization criteria, the process terminates, and it is assumed that this optimal string is the optimal solution to the problem being solved. If it does not meet the optimization criteria, a new generation of individuals is randomly selected, and crossover and mutation operations are performed on the population. The resulting individuals are selected according to the fitness of the newly generated offspring individuals.

[0111] The search performance of population-based genetic algorithms is very powerful and has shown many advantages in a wide range of optimization problems. The basic characteristics of genetic algorithms are as follows:

[0112] 1) Genetic algorithms encode feasible solutions instead of directly operating on feasible solutions;

[0113] 2) Genetic algorithms search for the optimal solution from the population rather than searching for an individual;

[0114] 3) The fitness function is used instead of the derivative;

[0115] 4) No prior knowledge and space restrictions are required;

[0116] 5) Use probabilistic transformation rules instead of deterministic rules.

[0117] Genetic algorithms have more independent individuals searching for the best solution in the solution space, which not only brings parallel processing capabilities but also minimizes the possibility of falling into local optimality. Genetic algorithms are simple and practical.

[0118] The performance of genetic algorithms depends on a set of control parameters, such as population size, crossover and mutation rates, and selection strategies. During the optimization cycle, the initial population is selected based on experience or modified appropriately, so that the population can show better search performance. Some theoretical and empirical studies also provide guidance for choosing an appropriate population size. The crossover rate controls the crossover frequency of the crossover operator. The higher the crossover rate, the faster new individuals join the population. The mutation operation is to maintain the diversity of the population, and it is also used to jump out of the local optimum.

[0119] Genetic algorithms can be easily applied to the constrained reactive power optimization problem proposed in this application. The reactive power transmitted by the wind turbine is used as the control variable of the optimization process to perform real number coding, and the population size is selected after experiments on several test systems. Since the optimization problem is confined to a limited part of the network, it is observed that the optimal population size is less sensitive to the system size. On the contrary, strict convergence criteria are found to be the dominant factor affecting the optimal population size. Fitness evaluation was performed on the simulation results, and it was found that the medium-sized population was satisfactory. The real number coding problem is roulette selection, arithmetic crossover and non-uniform mutation. Crossover rates of 20%-100% and mutation rates of 0%-60% were tested in several test systems and several accidental events, and it was found that lower crossover rates and higher mutation rates have stronger optimization capabilities from the perspective of computational speed.

[0120] Specifically, this application uses the wind turbine reactive power reference value and the PCC node reactive power demand as control variables, and the wind farm reactive power optimization dispatch control process based on the genetic algorithm is as follows:

[0121] 1) Set the population size, the maximum number of iterations, and randomly generate an initial population with a fixed length, where each individual represents a candidate solution. The parameters of the genetic operator are set as follows: (1) Selection method: roulette method; (2) Crossover: modifiable single-point crossover, with a crossover rate of 0.7; (3) Mutation: the mutation rate is set to 0.01; (4) Population size: set to 40; (5) Maximum number of iterations: set to 50;

[0122] 2) Evaluate the fitness function and calculate the fitness value of each chromosome in the population;

[0123] 3) Select the population with the highest fitness;

[0124] 4) Replicate populations based on probability and random methods;

[0125] 5) Perform a crossover operation on the replicated chromosome;

[0126] 6) Perform mutation operations on population chromosomes;

[0127] 7) Repeat step (2) until the predefined convergence criteria are met.

[0128] Furthermore, in the embodiment of the present application, the control parameters of the genetic algorithm are initialized, including:

[0129] Set the population size, crossover rate, mutation rate and maximum number of iterations based on experimental experience.

[0130] Figure 3 A schematic diagram of the structure of a wind farm reactive power optimization device based on a genetic algorithm provided in Example 2 of the present application.

[0131] like Figure 3 As shown, the wind farm reactive power optimization device based on genetic algorithm includes:

[0132] The first construction module 10 is used to establish a reactive power optimization mathematical model for the network loss of the doubly-fed wind farm collection system based on the active power loss of the doubly-fed wind farm transmission line and transformer, and to construct an objective function of the network loss of the doubly-fed wind farm collection system according to the reactive power optimization mathematical model for the network loss of the doubly-fed wind farm collection system;

[0133] The second construction module 20 is used to construct the constraint conditions of the network loss of the doubly-fed wind farm collection system according to the reactive output limit of the wind turbine of the doubly-fed wind farm, the node voltage, the branch apparent power, and the reactive demand of the grid connection point voltage stability, and use the penalty function to process the constraint conditions, and obtain the constrained objective function according to the objective function and the processed constraint conditions;

[0134] The optimization module 30 is used to optimize and calculate the reactive power reference value of the doubly-fed wind turbine based on the objective function with constraints by using a genetic algorithm.

[0135] The wind farm reactive power optimization device based on genetic algorithm in the embodiment of the present application includes a first construction module, which is used to establish a reactive optimization mathematical model of the network loss of the doubly fed wind farm collection system based on the active power loss of the doubly fed wind farm transmission line and transformer, and construct an objective function of the network loss of the doubly fed wind farm collection system according to the reactive optimization mathematical model of the network loss of the doubly fed wind farm collection system; a second construction module, which is used to construct the constraint conditions of the network loss of the doubly fed wind farm collection system according to the reactive output limit of the wind turbine of the doubly fed wind farm, the node voltage, the branch apparent power, and the reactive power demand of the grid connection point voltage stability, and use a penalty function to process the constraint conditions, and obtain a constrained objective function according to the objective function and the processed constraint conditions; an optimization module, which is used to optimize and calculate the reactive output reference value of the doubly fed wind turbine using a genetic algorithm based on the constrained objective function. In this way, the technical problem of large network losses in the existing wind farm collection system (active power losses in transmission lines and transformers) can be solved. By establishing a reactive optimization mathematical model for the network losses of the wind farm collection system and then using a genetic algorithm to optimize the calculation of the reactive output reference value of the doubly fed wind turbine, the network losses of the wind farm collection system can be effectively reduced.

[0136] In order to implement the above embodiments, the present application also proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the wind farm reactive power optimization method based on genetic algorithm described in the above embodiments is implemented.

[0137] In order to implement the above embodiment, the present application also proposes a non-temporary computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the wind farm reactive power optimization method based on a genetic algorithm of the above embodiment is implemented.

[0138] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0139] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0140] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0141] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0142] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0143] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0144] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0145] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for optimizing reactive power of a wind farm based on a genetic algorithm, characterized in that: The following steps are involved: Based on the active power loss of the transmission line and transformer of the doubly-fed wind farm, a reactive power optimization mathematical model of the network loss of the doubly-fed wind farm collection system is established, and according to the reactive power optimization mathematical model of the network loss of the doubly-fed wind farm collection system, an objective function of the network loss of the doubly-fed wind farm collection system is constructed; According to the reactive output limit of the wind turbine of the doubly-fed wind farm, the node voltage, the branch apparent power, and the reactive demand for the voltage stability of the grid connection point, the constraint conditions of the network loss of the collection system of the doubly-fed wind farm are constructed, and the constraint conditions are processed using a penalty function, and the objective function with constraints is obtained according to the objective function and the processed constraint conditions; Based on the constrained objective function, a genetic algorithm is used to optimize and calculate the reactive output reference value of the doubly-fed wind turbine.

2. The method according to claim 1, characterized in that The reactive power optimization mathematical model of the network loss of the doubly-fed wind farm collection system is expressed as: in, and Represents the active power loss of each transformer and each cable, N trans Indicates the number of transformers, N L Represents the transmission line sub-index.

3. The method according to claim 2, characterized in that The objective function of the network loss of the doubly-fed wind farm collection system is expressed as: Among them, P loss represents the network loss of the wind farm collection system, P WTi represents the active output of the i-th wind turbine, P PCC It indicates the measured active value of the high-voltage busbar of the main transformer of the wind farm.

4. The method according to claim 1, characterized in that The method of constructing the constraint conditions of the network loss of the collection system of the doubly-fed wind farm according to the reactive output limit of the wind turbine of the doubly-fed wind farm, the node voltage, the branch apparent power, and the reactive demand of the grid connection point voltage stability, and processing the constraint conditions using a penalty function, includes: According to the reactive output limit of the wind turbine of the doubly-fed wind farm, the node voltage, the branch apparent power, and the reactive demand for voltage stability at the grid connection point, the wind turbine reactive output constraint conditions, the node voltage constraint conditions, the branch apparent power constraint conditions, and the reactive demand constraint conditions of the network loss of the doubly-fed wind farm collection system are constructed; The wind turbine reactive output constraint, node voltage constraint, and branch apparent power constraint are processed using a penalty function, and after the reactive demand constraint is converted into an inequality constraint, the converted constraint is processed using a penalty function to obtain a processed constraint; wherein, The wind turbine reactive output constraint, the node voltage constraint, the branch apparent power constraint and the reactive demand constraint are expressed as: Among them, Q WTi Represents the reactive power output of each fan, U j represents the voltage of the jth node, S j represents the apparent power of each j branch, N bus Indicates the number of buses, N WT Indicates the total number of fans; The processed constraints are expressed as: Among them, w1, w2 and w3 represent the weights of the penalty function, N bus Indicates the number of buses, N WT Indicates the total number of fans.

5. The method according to claim 4, characterized in that The constrained objective function is expressed as: Among them, f is the penalty function, the expression is: P losses represents the objective function, w1, w2 and w3 represent the weights of the penalty function, v represents the reactive power reference value of the wind turbines participating in the dispatch, u represents the node voltage, Indicates the reactive power measurement value of the grid connection point.

6. The method according to claim 1, characterized in that The method of optimizing and calculating the reactive power reference value of the doubly-fed wind turbine based on the constrained objective function using a genetic algorithm comprises: Initializing the control parameters of the genetic algorithm and randomly generating an initial population with a fixed length, wherein each individual in the population represents a candidate solution; The objective function with constraints is used as a fitness function, and the fitness of each individual in the population is calculated according to the active power loss of the wind farm and the reactive power demand of the PCC point; According to the fitness, it is judged whether the corresponding individual meets the preset optimization standard. If not, the population with the highest fitness is copied based on probability and random methods, and crossover and mutation operations are performed to generate a new population. The fitness of each individual in the new population is calculated until the preset optimization standard is met, and the reactive output reference value of the doubly fed wind turbine is obtained.

7. The method according to claim 1, characterized in that The initializing the control parameters of the genetic algorithm includes: Set the population size, crossover rate, mutation rate and maximum number of iterations based on experimental experience.

8. A wind farm reactive power optimization device based on genetic algorithm, characterized in that: include: The first construction module is used to establish a reactive power optimization mathematical model of the network loss of the doubly-fed wind farm collection system based on the active power loss of the doubly-fed wind farm transmission line and transformer, and to construct an objective function of the network loss of the doubly-fed wind farm collection system according to the reactive power optimization mathematical model of the network loss of the doubly-fed wind farm collection system; The second construction module is used to construct the constraint conditions of the network loss of the collection system of the doubly fed wind farm according to the reactive output limit of the wind turbine of the doubly fed wind farm, the node voltage, the branch apparent power, and the reactive demand of the grid connection point voltage stability, and use the penalty function to process the constraint conditions, and obtain the constrained objective function according to the objective function and the processed constraint conditions; The optimization module is used to optimize and calculate the reactive power reference value of the doubly-fed wind turbine based on the constraint-containing objective function by using a genetic algorithm.

9. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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