Reactive Power Planning in Large Power Systems using Genetic Algorithm

Abstract

This paper presents a new approach to optimal reactive power planning based on a genetic algorithm which is a kind of search algorithm based on the mechanics of natural selection and genetics. This algorithm can search for a global solution using multiple path and treat integer problem naturally. In the method, discrete control variables are represented as integer strings (chromosomes). Two intentional operations are proposed to improve this chromosomes in addition to conventional operations. First one is “interbreeding” which crosses preferable subsystems each other to form a better combination of control variables. Second one is “manipulation” which improves power system profiles using AI-based stochastic “If-then” rules. Since “If-then” rules are deterministicly applied in expert systems, rules should be built without any conflict. The proposed method, however, applies such “If-then” rules stochastically. The proposed method was applied to practical 51-bus and 224-bus systems to show its feasibility and capabilitys. In both systems, the method shows very good convergent characteristics to global solutions in discrete value. During this research, many global or quasi-global solutions are found. It will be worthwhile to investigate these phenomena as further works.