Balusu Srinivasa Rao and Kanchapogu Vaisakh
Artificial immune system, clonal selection algorithm, optimal power flow, multi-objective adaptive clonal selection algorithm, wind energyconversion systems, load uncertainty
In this paper a solution to multi-objective optimal power flow (OPF) problem using an adaptive clonal selection algorithm (ACSA) with the incorporation of wind energy conversion system (WECS) is presented. As the wind power is stochastic in nature it requires an appropriate tool for OPF problem. Minimization of transmission loss, voltage stability index (L-index) and operating cost of thermal and wind units are three conflicting objectives for optimal operation and control of power system. Hence, multi-objective ACSA (MOACSA) is proposed to get Pareto optimal set of solutions. In this algorithm, a non-dominated sorting and crowding distance have been used to find and manage the Pareto optimal front. Further, a fuzzy-based mechanism has been used to select a best compromise solution from the Pareto set. This paper also presents a methodology to incorporate the effect of load uncertainty into OPF problem in addition to WECS by using MOACSA method. The proposed MOACSA has been tested on IEEE 30-bus test system having three conventional and three wind power generators. Simulation studies are carried out under both normal and load uncertainty conditions. The results are analysed and compared with three other standard algorithms namely non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective particle swarm optimization (MOPSO) and multi-objective differential evolution (MODE).
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