Research On Distribution Reactive Power Optimization Base On Modified Genetic Algorithm

In recent decades, China’s power industry is also obtained great progress with the rapid development of Chinese economy, at the same time, the earth’s energy has reserves had declined sharply and the ecological environment had deteriorated constantly, therefore, energy-saving emission reduction has become our country and even the whole world the important task, for the power industry, how to enhance power quality and energy loss management is way that reduces the energy consumption of important, research shows that, in the distribution network loss power approximately seventy percent in line loss, line loss not only affects the economic benefits of power supply enterprise, but also caused social energy waste, therefore, through the distribution network reactive power optimization in distribution network loss reduction power is not only of theoretical significance, but also has a strong practical value, at the same time, the reasonable distribution of reactive power in distribution network for improving the power quality has important significance.The first chapter introduces the purpose and significance of the thesis, summarizes the distribution network reactive power optimization research status at home and abroad, and paper summarized the work;The second chapter introduces reactive power optimization mathematical model, and to determine the objective function in the form used at the Institute of this article;The third chapter introduces the development and basic principles of the genetic algorithm, through the introduction of the basic genetic algorithm, to understand the basic operation of the genetic algorithm, improved genetic algorithm, and through multi-peak function optimization in multi-application verify the effectiveness of improved genetic algorithm;The fourth chapter first introduces the reactive power optimization flow calculation method used by the Institute of the distribution network; Second, research on Improved Genetic Algorithm in Reactive Power Optimization, given the specific operation of the genetic algorithm details; Finally, through the analysis of specific examples, verified the excellent performance of the improved genetic algorithm.