An Efficient Adaptive Moth Flame Optimization Algorithm for Solving Large-Scale Optimal Power Flow Problem with POZ, Multifuel and Valve-Point Loading Effect

Abstract

This paper offers an enhanced adaptive moth flame optimization (AMFO) algorithm to solve the optimal power flow (OPF) problems efficiently. The idea of moth flame optimization (MFO) is motivated by the movement of moth headed about the moon direction. AMFO is primarily centered on the notion of MFO with adjusting the direction of moths in an adaptive manner around the flame. AMFO is compared with standard MFO for 14 different benchmark test suites. Standard IEEE 118-bus test system is used to substantiate the effectiveness and robustness of AMFO algorithm. The authentication of the suggested algorithm is established on 12 case studies for various single-objective functions like fuel cost minimization, emission minimization, active power loss minimization, voltage stability enhancement and voltage profile improvement. The simulation findings of the suggested algorithm are compared with those found by other well-known optimization methods. The achieved results demonstrate the ability and strength of AMFO approach to solving OPF problems. The outcomes divulge that AMFO algorithm can obtain accurate and improved OPF solutions compared with the other methods. A comparison among the convergence qualities of AMFO and the different techniques demonstrates the predominance of AMFO to achieve the optimal power flow solution with rapid convergence.