Reactive Power Optimization Of Power System Based On The Improved Genetic Algorithm

Rational distribution of reactive power in power system is the prior condition which can ensure voltage quality and reduce the loss. Optimization adjustment of reactive power can act on secure and economical operation of power system. So reactive power optimization of power network is an important problem which directly influences voltage quality of the system as well as which relates to economical operation of power network. During the previous calculation of reactive power and voltage in the power network, reactive power insufficiency and system under-voltage are always concerned.The measure of Reactive power optimization mainly considers on-load tap changer, the optimal capacity of the capacitor, the voltage of generator under the steady load. Reducing active power loss is considered of the main object function in this paper. The model of reactive optimization was established based on these. And the penalty function is considered to deal with variables violating the constraints.In this paper, genetic algorithm is applied in solution of reactive power optimization. Aiming at premature and slow convergent speed which happens easily in the cause of solving reactive power optimization which belongs to complicated Non-linear optimization, this paper improves the arithmetic. Tournament selection is used in the selection operation. Metropolis receive principle of simulated annealing is applied after the cross operation, and the algorithm adopts self-fit cross rate and mutation rate, and the second mutation is applied at the later period of mutation operation. Improved algorithm can avoid converging to a local optimal solution, and the speed and precision are boosted to a certain extent, meanwhile maintain custom algorithm virtues.Based on the proposed mathematical model and algorithm, practical program is made by MATLAB language. The proposed algorithm in this paper is applied to the IEEE 30-bus system, and compares the result of adaptive genetic algorithm andtabu search genetic algorithm, all the results given in the paper show that the consistency of the genetic algorithm control strategy, the improved genetic algorithm with sensitivity analysis is stable and fast and suitable for practical application medium-scale power system. At the same time, it verifies the model and algorithm practical, reliable and effective.