Reactive Power Optimization, Power System Based On Improved Genetic Algorithm

The reactive power optimization of a power system can efficiently minimize the real power losses and improve voltage level of it ,so that the yearly running charge will be reduced and quality of power energy can be increased. Study the problem of reactive power optimization has the great significance in theory and practical application. This dissertation expatiates the purpose and practical meaning of power system reactive power voltage optimal control, introduces the develop course and up-to-date trends in the research of reactive power optimization problems, analyzes the advantage and disadvantage and applicable scope and presents the researching direction of reactive power voltage optimization.Power flow is the base and tool of reactive power optimization, whose convergence and computation speed is very important to the efficiency of optimization. This paper deeply analyses the power flow method of a power system, and chooses fast decouple load flow containing the merits of simpleness,high compution speed, EMS memory save and good convergence to compute load flow.Reactive power optimization problem is a large-scale nonlinear optimization problem with a large number of variables and uncertain parameters, the operating variables include continuous and discrete variables, so the optimization becomes very complex.In this paper ,the objective function is to minimize the active power loss , the node voltage of system and reactive power of generators which are step beyond the boundary are appended to it as punished functions.This dissertation adopts genetic algorithm as optimization algorithm and makes some developments to genetic algorithm. The developments can be divided into two parts: First part, Adaptive strategy based on phase evolution and niche genetic algorithm are adopted along with developments in coding, fitness function, select operator, crossover operator, mutation operator and evolution end condition to overcome the disadvantage of being prone to be premature of simple genetic algorithm. Second part, this dissertation combines power system real power loss sensitivity index and genetic algorithm, adopts loss sensitivity to guide mutation operation in genetic algorithm which ensures that the individual after mutation is better than original individual (that is to say, the new individual has a smaller loss), which assists genetic algorithm in searching for global best solution.In order to prove the effect of improved genetic algorithm, this dissertation uses this algorithm to calculating the IEEE30 bus system. The result shows that the loss-reduce is quite considerable and the voltage eligibility rate is a hundred percent. These results show that the improved genetic algorithm can overcome the phenomenon of being prone to be premature in a certain extent and has a good optimization effect.