Improvement And Application Of Chicken Swarm Algorithm In Reactive Power Optimization

With the rapid development of China’s economy,the structure of power grid is increasingly complex,and users have higher and higher requirements for power quality,so it is increasingly important to ensure the safe,stable,reliable and economic operation of power grid.The RATIONAL distribution of reactive power flow is of great significance to power system.Aiming at the problem of reactive power optimization,this paper uses chicken swarm algorithm to carry out reactive power optimization and to improve the defects of chicken swarm algorithm.The following work is carried out in this paper:Firstly,the background,purpose and significance of reactive power optimization problem are introduced.At the same time,the traditional optimization algorithm and artificial intelligence algorithm for solving reactive power optimization problem are briefly introduced.The basic principles,advantages and disadvantages of the traditional optimization algorithm and artificial intelligence algorithm are analyzed.Secondly,introduces the mathematical model of reactive power optimization and power flow calculation method,determine the objective function used in this paper,equality constraints and inequality constraints,and the use of power flow calculation method,and then introduces the basic principle of particle swarm optimization and differential evolution algorithm,individual update rules and algorithm process,analyzes the individual update formula of the meaning of each parameter and effect the performance of the algorithm,provide the reactive power optimization results contrast objects.Thirdly,the basic principle,position updating rules and the process of the chicken swarm algorithm are introduced,and the influence of each parameter in the position updating formula of the chicken swarm algorithm on the algorithm is analyzed.This paper analyzes the advantages and disadvantages of the chicken swarm algorithm,and introduces reverse learning,Cauchy mutation and learning factor improvement strategy to improve the chicken swarm algorithm,gives the algorithm flow of the improved chicken swarm algorithm,and uses six test functions to test the improved chicken swarm algorithm.Finally,the four algorithms are programmed and simulated in MATLAB to solve the reactive power optimization problem,and applied to the reactive power optimization problem of IEEE-30 node and 57 node system.The effectiveness and feasibility of the improved algorithm are verified by comparing and analyzing the optimization results of the four algorithms.