Reacive Power Optimization Of Power System Based On Improved Artificial Searching Swarm Algorithm

Along with the continuous improvement of our country economy,science and technology,the scale of power system is rapidly expanding.It includes that advanced power technology have being applied on the power system network,and the connotation is rapidly changing.Which increases the randomness,uncertainty and non predictability of the reactive power optimization in power system.Because of these new challenges,it has being one of the most hot issues in the field of power system optimization.The optimization algorithm of Reactive power optimization include the bionic intelligent optimization algorithm and the conventional mathematical algorithm,so far.In the face of the inverse problem of reactive power optimization with multiple constraints,nonlinear,discrete and continuous variables,the conventional mathematical algorithm is easy to fall into the local optimal solution.Bionic intelligent optimization method is widely used in the field of reactive power optimization in power system,with its high flexibility,high robustness,strong adaptability and so on.Artificial Searching Swarm Algorithm(ASSA)is a novel intelligent optimization algorithm.Selecting six typical functions,in this paper,are used to test and compare the performance of ASSA,AFSA and PSO.The results show the ASSA has better searching ability,in the optimization design,than the others.There are still some deficiencies in the optimization process of the artificial search algorithm,so some improvement strategy is proposed in this paper.Owing to the initial population has a significant impact on the performance of the algorithms,this paper introduces the chaos mechanism to generate initial population and proposes the Chaos Artificial Searching Swarm Algorithm(CASSA).Secondly,the step parameter of the artificial search algorithm is improved,introduces dynamic step and ICASSA is proposed.Selecting typical functions,in this paper,are used to test and compare the performance of ICASSA and ASSA.The results show the ICASSA has better searching ability,in the optimization design,than the ASSA.In the MATLAB2014 b environment,ICASSA is used to optimize the simulation of the IEEE14 and IEEE30 buses of the standard power system.Comparing the performance of ICASSA,IPSO and IDE,the results show the ICASSA has better searching ability.Finally,the reactive power optimization of a real power network is simulated,and the results show that the ICASSA algorithm has potential application value.