Moth flame optimization to solve optimal power flow with non-parametric statistical evaluation validation. Issue 1 (1st January 2017) - Buch, Hitarth; Trivedi, Indrajit N.; Jangir, Pradeep
Abstract: This article demonstrates an appositeness of a novel metaheuristic optimization algorithm viz. the moth flame optimization (MFO) to solve various non-convex, non-linear optimum power flow (OPF) objective functions. MFO is based on movement of moths with respect to the source of light. In this paper, five single objective functions are selected for solving the OPF problem: generator fuel cost minimization under various realistic conditions, real power loss reduction, and emission minimization. Simulations are performed on the IEEE 30-bus system to identify efficacy of the proposed method. Results obtained by MFO are collated with other stochastic methods reported in literature. Comparison reflects that MFO obtains optimum value with rapid and smooth convergence. Statistical tests like Wilcoxon test, Quade test, Friedman test and Friedman aligned test are also carried out to check the effectiveness of the MFO. Comparison of MFO with other stochastic algorithms demonstrates superiority of MFO in terms of solution excellency and solution feasibility, substantiating its effectiveness and competence.