Research On Reactive Power Optmization Of The Radial Distribution System

SGA has defects of slow convergence and being prone to immature convergence. In order to eliminate the defects, an improved GA is proposed in this thesis. This thesis suggests a process considered minimizes the population size as similar individuals occur in the fitter members of the population, which helps reduce the execution times for GA by removing the redundancy associated with the saturation effect found in the later generation. This thesis uses a method that adds dynamic penalty terms to the fitness function according to the optimal degree of solutions, so as to create a gradient toward a feasible suboptimal or even optimal solutions. On the basis of the difference of the biggest and the smallest of Fitness of individual, modifying the fitness function in order to convergence is a satisfaction. In order to avoid inbreeding, competitive crossover is used. Making use of the idea of the parallel genetic algorithms, presenting the adaptive multiple subpopulation evolutive strategy. presenting extinction and immigration strategy in order to avoid the similar or even same individuals appear. In order to enhance convergence velocity of reactive power optimization of the radial distribution system, combining the characteristic of the radial distribution system, a sensitivity analysis approach was build up for optimal selection of capacitors and mutation of transformer tap changer. Heuristic mutation capacitors basing on practice are proposed. Taking load uncertainties, infeasibility problem and multiple objection of the reactive power optimization in the radial distribution system into consideration, loads are modeled as fuzzy interval numbers. Fuzzy power flow is proposed based on fuzzy interval load for the more practical membership function of line losses rate and maximal voltages offset. This thesis presents multiple objection model of the reactive power optimization considering uncertainties using the fuzzy set theory. The model is do by IGA and fuzzy set. The optimal solution is the one with maximum value of the satisfaction parameter.