Study On Evolutionary Programming Reactive Power Optimization Algorithm Based On Specialist Experiences

Reactive power optimization which can improve the quality of system voltage and reduce reactive power loss has not been solved completely because of the complexity of mixed-integer nonlinear programming. Optimized routing algorithm of feasible power flow is proposed in this paper combining with evolutionary programming and the basic principle of system voltage reactive power optimization.This method is heuristic and gets great efficiency. It analyzes the factors such as the partition of all the same layer, net power flow and distribution level to optimized routing adjustment of reactive power and voltage regulation of all power plants and substations, including local and globe. It makes power flow available and meet the need of the reactive power balancing and the reverse voltage regulation by combining the reactive power balancing principle of voltage-grading & district-dividing and local reactive power compensation with feasible power flow regulation, the reverse voltage regulation of central point voltage. Few reactive power voltage regulations and power flow calculations with this algorithm can ensure the results of power flow and reduction loss. The efficiency of controlling actual system voltage and reactive power optimized control and operation is proved by simulation examples in this paper.A new reactive power optimized evolutionary programming algorithm has been proposed through the heuristic experiences of reactive power optimized voltage regulation and adaptive evolutionary programming algorithm. With these experiences the algorithm can control mutation direction of reactive power optimized multivariable and adjustment of relative local and global control multivariable by analyzing the non- feasible reason of individual power flow. Finally the mutation efficiency of non-feasible individual control multivariable are being improved. adaptive mutation algorithm has been adopted to ensure the global random searching speciality for feasible individual. The global random searching ability of adaptive evolutionary programming algorithm and high evolutionary ability by the method in this paper is illustrated by the result of actual power systems.