A hybrid moth–flame algorithm with particle swarm optimization with application in power transmission and distribution

Abstract

The transmission lines are used for power distribution across large distances. Different parameters affect the power transmission efficiency, and the quality of service. Furthermore, the transmission system parameter estimation is crucial for power flow analysis, electric power system expansion planning, stability, dispatch, and economic analysis. This task is created by utilizing system identification techniques, and with the analytical method being the most commonly utilized methodology for acquiring transmission line data. However, to address an issue that simplifies the estimation these techniques have significant downsides — such as non-recursive parameter estimation and the accessibility of an appropriately transposed line. This paper presents a hybrid moth-flame optimization (MFO) and particle swarm optimization (PSO) for estimating transmission line parameters based on various scenarios and mathematical validation on different benchmark functions. The concepts of MFO and PSO are rationally integrated into this algorithm to overcome their limitations and improve their global search ability. Regarding solution quality and convergence speed the results show that the proposed hybrid algorithm performs better than the conventional PSO and original MFO.