Reactive Power Optimization Of Distribution Network With Doubly-Fed Induction Generator Based On Improved Multi-Objective Multi-Verse Optimizer

With the sharp rise in global energy prices,all countries have begun to attach importance to new energy power generation,such as wind power generation.Due to its advantages in investment cost and installation methods,wind power generation takes the lead in new energy power generation,so it has been established as one of the key development directions of my country’s power industry in the future.However,due to the randomness and volatility of the wind speed itself,the wind turbine will have a negative impact on the node voltage level of the distribution network when it is connected to the grid.Through relevant research,it can be seen that reactive power optimization can effectively stabilize the system voltage level and significantly reduce the sum of system network losses.Therefore,adjusting the adjustable reactive power output in the system can be used as a means to solve the problem of wind turbine grid connection.However,due to the increase of control variables in the distribution network incorporating wind turbines,the efficiency of traditional reactive power optimization algorithms in solving such problems is significantly reduced.Therefore,artificial intelligence algorithms need to be introduced to speed up the calculation efficiency of reactive power optimization problems.In this paper,the following researches are done for the reactive power optimization problem of the distribution network with double-fed fans:1)The structure principle of the DFIG is deeply studied,and the active and reactive output characteristics of the DFIG are calculated through its technical parameters,and the gridconnected form of the DFIG is discussed.For the distribution network with doubly-fed wind turbines,the forward-backward algorithm is used as the power flow calculation method for the distribution network.2)Aiming at the randomness of wind speed,the Latin hypercube sampling method was used to simulate the wind speed,and a large number of wind speed samples were obtained.According to the characteristics of the samples,the K-means clustering algorithm was used to reduce the samples,and finally the typical scene of the fan output was established.3)The basic principles of the multi-objective multiverse algorithm are introduced.In view of the problem that the multi-objective multi-universe algorithm is easy to fall into local optimum in the solution process,an improved multi-objective multiverse algorithm is proposed,and the effectiveness of the proposed algorithm is verified by standard test functions.4)A multi-objective reactive power optimization model is established with the minimum system active network loss and the minimum node voltage offset as the objective function,and the improved multi-objective multiverse algorithm is used to optimize and solve the model,and other algorithms are used to compare and verify the effectiveness of the proposed algorithm sex.The effectiveness and feasibility of the proposed method are verified by the simulation of the improved IEEE-33 node system,and the influence of the reactive power output of the wind turbine on the node voltage and the total network loss of the system in multiple scenarios is discussed.