Implementation of genetic and particle swarm optimization algorithm for voltage profile improvement and loss reduction using capacitors in 132 kV Manipur transmission system

Complex power plays a key role in maintaining and sustaining the fields i.e. magnetic and electric fields. The efficiency of the power system depends entirely on the loss and voltage profile level of the system. The lower the level of loss and the higher the voltage profile, the higher the system efficiency. Installation of the capacitors on a system greatly helps in controlling and reducing levels of the reactive energy of the system. The location and size of the capacitors that need to be installed are of great importance in the installation of the bank of capacitors. The idea of a Genetic Algorithm and also a Particle Swarm Algorithm are deployed in this paper for calculating the optimal location and size of the capacitor. In this paper, the system of the 132 KV Manipur Transmission System is designed in ETAP considering a radial distribution part and the optimization method of a genetic method, and also the particle swarm algorithm is utilized for finding the optimal location and size of the capacitor. The system is further optimized by reducing the loss and improving the voltage profile of the system. And the comparison of both the optimization techniques is presented. The results show that PSO provides optimal solutions while GA is simpler in the optimization process. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under theCCBYlicense (http://creativecommons.org/licenses/by/4.0/).

Automated summary

Complex power plays a crucial role in maintaining and sustaining the magnetic and electric fields. The efficiency of a power system relies on the system's loss and voltage profile. Installing capacitors helps control and reduce reactive energy levels. This paper utilizes genetic and particle swarm algorithms to determine the optimal location and size of capacitors, aiming to optimize the 132 KV Manipur Transmission System by reducing losses and improving voltage profiles. Results show that Particle Swarm Optimization (PSO) provides optimal solutions, while Genetic Algorithm (GA) is simpler in the optimization process.