Gbest-Guided Artificial Bee Colony Optimization Algorithm-Based Optimal Incorporation of Shunt Capacitors in Distribution Networks under Load Growth

Abstract

In this work, a new technique is introduced for optimal incorporation of shunt capacitors (SCs) in distribution networks. This technique has been compared to other sensitivity-based approaches such as loss sensitivity factor, index vector method, power loss index, and index of voltage stability. In the proposed technique, the optimal positions as well as the ratings of SCs are identified through an optimization algorithm. In sensitivity-based approaches, the positions of SCs are determined through a sensitivity approach and the optimal ratings of SCs are computed through an optimization algorithm. The main target of this study is to minimize the total annual cost and power loss of the network under load growth. This has been done through the population-based Gbest-guided artificial bee colony (GABC) optimization technique. Furthermore, the outcomes obtained through the GABC algorithm are compared to those from the iteration particle swarm optimization algorithm. The whole work along with the proposed methodology has been demonstrated on standard 34-bus and 118-bus distribution networks for SC placement. The results show that it reduces the total annual expense of the network to a great value. Consequently, it improves the total power loss reduction, enhances the voltage profile and power factor, and reduces the total voltage deviation. The obtained numerical outcomes through the proposed technique have been compared with the published literature outcomes to show the viability and superiority of the algorithm.

Keywords

• optimal capacitor placement
• ipso
• gabc algorithm
• lsf
• ivs
• pli
• ivm
• load growth