An Improved Multi Objective Mayfly Algorithm for Solving Optimal Power Flow Problem Considering Different Loading Conditions

Abstract

This paper presents an improved multi-objective mayfly algorithm (IMOMA) to resolve the optimal power flow (OPF) problem in a regulated power system network with different loading conditions. The OPF problem, considered a multi-objective optimization problem, comprises multiple objective functions related to economic, technical, operational and security aspects. The IMOMA algorithm has been developed by implementing the simulated binary crossover (SBX), polynomial mutation and dynamic crowding distance (DCD) operators in the original multi-objective mayfly algorithm (MOMA).The OPF problem is analyzed by considering multiple objective functions in the IEEE30-bus test system, the IEEE118-bus test system and the 62-bus Indian utility system. The hypervolume performance metric is used to compare the performance of the MOMA and IMOMA with respect to different operating scenarios. Further, loading conditions ranging between 150% and 50% of the base load are considered for the evaluation. The effectiveness of the IMOMA over the MOMA is observed from the results of the different loads. The best compromise solution is obtained from a set of pareto optimal solutions by implementing the TOPSIS method.