Abstract
This work presents a novel Moth Swarm Algorithm (MSA), inspired by the orientation of moths towards moonlight to solve constrained Optimal Power Flow (OPF) problem. The associative learning mechanism with immediate memory and population diversity crossover for Lévy-mutation have been proposed to improve exploitation and exploration ability, respectively, in addition to adaptive Gaussian walks and spiral motion. The MSA and four heuristic search algorithms are carried out on the IEEE 30-bus, 57-bus and IEEE 118-bus power systems. These approaches are applied to optimize the control variables such as real power generations, load tap changer ratios, bus voltages and shunt capacitance values under several power system constraints. Fourteen different cases are executed on different curves of fuel cost (e.g., quadratic, valve-loading effects, multi-fuels options), environmental pollution emission, active power loss, voltage profile and voltage stability for contingency and normal conditions, in single and multi objective optimization space. Furthermore, the impacts of the updating mechanism of optimizers on those objective functions are investigated. The effectiveness and superiority of the MSA have been demonstrated in comparison with many recently published OPF solution
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