Employment of PSO algorithm to improve the neural network technique for radial distribution system state estimation

Abstract

Abstract This study presented a new hybrid algorithm to improve the state estimation (SE) of radial distribution power systems (PSs). The proposed particle swarm optimization–neural network (PSO–NN) algorithm constructed an independent and fast monitoring system with high accuracy that can detect abnormal conditions or failures in a PS. In this study, PSO was adopted to determine the appropriate weights of the NN model. The speed and accuracy of PSO with the NN model were evaluated in the SE of power system networks (PSNs). The information obtained through SE was used to enhance the operations and customer service delivery in terms of energy consumption and power quality in PSNs. Capacitor banks were installed to reduce the losses and improve the voltage profiles. The PSO–NN algorithm was assessed on IEEE (9, 33, and 69) bus standards. Simulation results proved that the new technique can be tested on any distribution network because of its accurate and efficient SE. Results indicated that the PSO–NN algorithm had better performance than the phasor measurement units.