Research On Reactive Power Optimization Of Power System Based On Double Fish-Swarm Algorithm

Maintaining the high quality and stable operation of power system is the main goal of reactive power optimization.The reasonable distribution of reactive power can effectively reduce the network loss and improve the voltage quality.Complex,nonlinear and mixed programming are the essential features of reactive power optimization.How to deal with the coexistence of continuous variables and discrete variables is a difficult problem.Until now,how to establish a practical and effective mathematical model is still a research focus in the field of reactive power optimization.However,the intelligent algorithm with random search features shows great adaptability in the reactive power optimization control system,which has showed good convergence characteristics.The convergent speed and quality of the algorithm are improved.Therefore,the problems and methods of reactive power optimization are thoroughly studied in this thesis,in order to obtain effective optimization results,reduce the system loss and improve the voltage level.Firstly,the research status of reactive power optimization is analyzed in this thesis,and the reactive power optimization method is deeply studied.The relationship between reactive power and voltage level,reactive power and active power loss are analyzed.The importance of reactive power balance is given.Objective function aims at the minimum of the active power loss and voltage deviation,and the reactive power optimization model is established by using the a method and penalty function,which is based on the theory of reactive power optimization.The dynamic penalty coefficient is adopted to enhance the adaptability of the algorithm in this thesis.Secondly,this paper studies the artificial fish swarm algorithm and its application in reactive power optimization are thoroughly studied in this thesis.A double fish-swarm algorithm is proposed to solve reactive power optimization problems,so that disadvantages of the artificial fish swarm algorithm can be improved.The behavior and optimizing process of fish-swam are redefined,which is divided into ferocious fish and small fish.The escape factor,foraging factor,predation factor and tracing factor are introduced to improve the fish behavior.The principle of selection of fish swarm parameters is given in this thesis.The dynamic step and visual range are used to improve the performance of the algorithm.The optimization performance of double fish-swarm algorithm is verified through the four test functions.Finally,the double fish-swarm algorithm is used to solve the reactive power optimization problem.The rationality of reactive power optimization model is verified by double fish-swarm algorithm.The comparison results show that the proposed reactive power optimization model is reasonable and can meet the requirements of each performance index.The basic artificial fish-swarm algorithm,the improved genetic algorithm,and the proposed algorithm have been applied to IEEE 14-bus system and IEEE 30-bus system,so as to test the effectiveness,respectively.The simulation results show that double fish-swarm algorithm has the best optimization effect in terms of computational accuracy and convergence stability,which is much more in line with the actual operation of the power system.