Research On Multi-Objective Reactive Power Optimization Based On Hybrid Coding Genetic Algorithm

Reasonable distribution of reactive power in power supply system is a precondition to ensure the safety,reliability and economic operation of the system.Reasonable distribution of reactive power can also effectively reduce the active power loss of the system and improve the voltage quality.Therefore,in order to improve the security,stability,economic benefits and operation efficiency of the current power supply system,to reduce the active power loss and improve the voltage quality of the power network as the optimization objective,combined with the related features of P-Q decomposition method for power flow calculation,this paper selects genetic algorithm,an artificial intelligence optimization algorithm,to optimize the distribution of reactive power in power system,and at the basic levelOn the basis of the genetic algorithm,improvements have been made to the coding method,initial population generation,selection,crossover and variation,and the improved genetic algorithm is used to configure the control variables in the power system reasonably in order to reduce the active network loss and improve the voltage quality of the power system.The specific work is as follows:(1)By consulting the relevant literature on reactive power optimization of power system,and summarizing the methods of reactive power optimization in power system in recent years,as well as common reactive power adjustment devices.After a preliminary understanding of the relevant principles and characteristics of genetic algorithm,according to the needs of this topic,combined with the actual situation of reactive power optimization and the relevant characteristics of genetic algorithm,to choose the appropriate optimization scheme.(2)After summarizing and studying the previous studies on the establishment of mathematical models,a mathematical model for reactive power optimization calculation of power system is established,which aims at reducing the active network loss of power supply system and improving the voltage quality.The relevant power constraint equations and variable constraint equations are listed.(3)After deeply learning the related knowledge of genetic algorithm,and after understanding the basic genetic algorithm's principle and steps,the advantages and disadvantages are analyzed,and on the basis of retaining its advantages,the shortcomings are improved to make the improved new genetic algorithm more suitable for solving the related problems of power system reactive power optimization.(4)This paper optimizes the traditional polar coordinate Newton Raphson method of power flow calculation by computer to P-Q decomposition method,then combines the established mathematical model with the improved new genetic algorithm,and uses MATLAB to program.(5)In this paper,three IEEE theoretical systems are simulated by the above compiled program,and the simulation results show that the optimized genetic algorithm has better convergence speed and accuracy than the original genetic algorithm,and can solve the related problems of reactive power optimization.In order to study the application of the optimized genetic algorithm in practice,some actual lines in Xuejiawan area of Inner Mongolia are simulated.The simulation results show that the optimized genetic algorithm can also solve the related reactive power optimization problems in the actual lines.