Multi-objective optimal power flow using stochastic weight trade-off chaotic NSPSO

Abstract

In this paper, a stochastic weight trade-off chaotic non-dominated sorting particle swarm optimization (SWTC_NSPSO) is proposed for solving multi-objective optimal power flow considering generator fuel cost and active power loss. The SWTC_NSPSO algorithm improves the solution search capability by balancing between global best exploration and local best utilization through the stochastic weight trade-off technique together with dynamistic coefficients trade-off methods. The chaotic mutation applied to the inertia weight factor enhances the search capability by diversifying the search space. Moreover, the non-dominated sorting and crowding distance techniques are used to provide the optimal Pareto front. Simulation results on IEEE 30-bus test system indicate that SWTC_NSPSO can provide a lower and wider Pareto front than non-dominated sorting genetic algorithm II (NSGAII), non-dominated sorting particle swarm optimization (NSPSO), non-dominated sorting chaotic particle swarm optimization (NS_CPSO), and a stochastic weight trade-off non-dominated sorting particle swarm optimization (SWT_NSPSO) leading to a lower generator fuel cost and a higher system reliability trade-off solution.