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Multi-objective enhanced PSO algorithm for optimizing power losses and voltage deviation in power systems

Gonggui Chen (Key Laboratory of Industrial Internet of Things and Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing, China AND Research Center on Complex Power System Analysis and Control, Chongqing University of Posts and Telecommunications, Chongqing, China AND Department of Electrical Engineering, Hubei Minzu University, Enshi, China)
Lilan Liu (Key Laboratory of Industrial Internet of Things and Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing, China)
Yanyan Guo (Department of Locomotive and Vehicle Engineering, Wuhan Railway Vocational College of Technology, Wuhan, China)
Shanwai Huang (Key Laboratory of Industrial Internet of Things and Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)
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Abstract

Purpose

For one thing, despite the fact that it is popular to research the minimization of the power losses in power systems, the optimization of single objective seems insufficient to fully improve the performance of power systems. Multi-objective VAR Dispatch (MVARD) generally minimizes two objectives simultaneously: power losses and voltage deviation. The purpose of this paper is to propose Multi-Objective Enhanced PSO (MOEPSO) algorithm that achieves a good performance when applied to solve MVARD problem. Thus, the new algorithm is worthwhile to be known by the public.

Design/methodology/approach

Motivated by differential evolution algorithm, cross-over operator is introduced to increase particle diversity and reinforce global searching capacity in conventional PSO. In addition to that, a constraint-handling approach considering Constrain-prior Pareto-Dominance (CPD) is presented to handle the inequality constraints on dependent variables. Constrain-prior Nondominated Sorting (CNS) and crowding distance methods are considered to maintain well-distributed Pareto optimal solutions. The method combining CPD approach, CNS technique, and cross-over operator is called the MOEPSO method.

Findings

The IEEE 30 node and IEEE 57 node on power systems have been used to examine and test the presented method. The simulation results show the MOEPSO method can achieve lower power losses, smaller voltage deviation, and better-distributed Pareto optimal solutions comparing with the Multi-Objective PSO approach.

Originality/value

The most original parts include: the presented MOEPSO algorithm, the CPD approach that is used to handle constraints on dependent variables, and the CNS method which is considered to maintain a well-distributed Pareto optimal solutions. The performance of the proposed algorithm successfully reflects the value of this paper.

Keywords

Acknowledgements

The authors would like to thank the editors and the reviewers for their constructive comments. This work is supported by the National Natural Science Foundation of China (Grant Nos 51207064 and 51507024), and Science and Technology Research Project of Chongqing Municipal Education Commission (Grant No. KJ1500401).

Citation

Chen, G., Liu, L., Guo, Y. and Huang, S. (2016), "Multi-objective enhanced PSO algorithm for optimizing power losses and voltage deviation in power systems", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 35 No. 1, pp. 350-372. https://doi.org/10.1108/COMPEL-02-2015-0030

Publisher

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Emerald Group Publishing Limited

Copyright © 2016, Emerald Group Publishing Limited

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