A Comprehensive Study of Improved Evolutionary Particle Swarm Optimization (IEPSO) for Network Reconfiguration with DGs Sizing Concurrently

Author:

Napis Nur Faziera1,Sulaima Mohamad Fani1,Abd Kadir Aida Fazliana1,Gan Chin Kim1,Mohd Dahalan Wardiah1,Sulaiman Marizan1

Affiliation:

1. Universiti Teknikal Malaysia Melaka

Abstract

This paper deals with the reconfiguration of the distribution network system to investigate the total power losses considering Distribution Generations (DGs) sizing concurrently. To overcome other limitations and enhance the solution performances, a new optimization approach called Improved Evolutionary Particle Swarm Optimization (IEPSO) is proposed. The primary aim of this study is to investigate the contribution of the proposed algorithms towards total power losses by considering the optimum DG size simultaneously. The proposed method is compared with the traditional Particle Swarm Optimization (PSO) and Improved Particle Swarm Optimization (IPSO) respectively. The amount of time that an algorithm spends in obtaining an alternative topological status for the system power loss reduction and distribution generation sizing is taken into consideration. In this context, the study is tested using IEEE 33 bus distribution system.

Publisher

Trans Tech Publications, Ltd.

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Dynamic Reconstruction of Distribution Networks with Distributed Photovoltaics Based on Weather Classification;2023 4th International Conference on Advanced Electrical and Energy Systems (AEES);2023-12-01

2. Opposition Based Constriction Factor Particle Swarm Optimization for Economic Load Dispatch;2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT);2022-04-21

3. Application of Hierarchical Encoding Scheme in Distribution Networks Reconfiguration;Arabian Journal for Science and Engineering;2018-07-27

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