Reactive Power Optimization In Power System Based On Improved Genetic Algorithm

With the increasing of power capacity and the electric equipment, the requirement of reactive power grows day by day. The unbalanced reactive power will cause the drop of power factor and voltage, the decline of capability of network transmission, and the increase of active power loss, so the equipments can not be sufficiently utilized, even be damaged. The reactive power optimization is an efficient method to guarantee the system's security and economically running and an important approach of decreasing the active power loss and enhancing the voltage quality, so it has the all-important theoretical value and practical significance.The reactive power optimization compensation is to find the suitable compensation place and the best compensation capacitance by adjusting the generator bus voltages, transform taps and reactive compensation capacities in the condition of satisfying power load requirement, which can guarantee the secure and high quality power for consumers.The improved genetic algorithm is applied in this thesis according to the features of reactive power optimization of high voltage transmission bus, which are the non-linear problems with multi-variables and multi-restrictions. The objective function is to minimize the active power loss and the system limitations are appended to it as punished functions. The improvements in this thesis include the hybrid code method, the method of generation of initial populations, substituting the children for parents by combination of the simulated annealing algorithm and niche, adding some new chromosomes to ensure the population's diversity and using the adaptive probability of crossover and mutation.The experiments of IEEE-6 and IEEE-30 systems indicate the improved genetic algorithm is correct and effective when applied in power system. Compared with standard genetic algorithm, it has the better convergent ability and speed.