Application of Moth-Flame Optimizer and Ant Lion Optimizer to Solve Optimal Reactive Power Dispatch Problems

Abstract

This paper presents the application of two nature-inspired meta-heuristic algorithms, namely moth-flame optimizer (MFO) and ant lion optimizer (ALO) in obtaining the optimal settings of control variables for solving optimal reactive power dispatch (ORPD) problems. MFO is developed by the inspiration of the natural navigation method of moths during night time while ALO is inspired by the natural foraging technique of antlions in hunting ants. These two algorithms are implemented in ORPD to determine the optimal value of generator buses voltage, transformers tap setting and reactive compensators sizing in order to minimize power loss in the transmission system. In this paper, IEEE 57-bus system is utilized to show the effectiveness of MFO and ALO. Their statistical results are compared against other metaheuristic algorithms. The results of this paper illustrate that MFO is able to achieve a lower power loss than ALO and other selected algorithms from literature.