Affiliation:
1. State Key Laboratory of Hybrid Process Industry Automation System and Equipment Technology, China Iron & Steel Research Institute Group, Beijing 100081, China
Abstract
This study presents a novel hybrid multiobjective particle swarm optimization (HMOPSO) algorithm to solve the optimal reactive power dispatch (ORPD) problem. This problem is formulated as a challenging nonlinear constrained multiobjective optimization problem considering three objectives, that is, power losses minimization, voltage profile improvement, and voltage stability enhancement simultaneously. In order to attain better convergence and diversity, this work presents the use of combing the classical MOPSO with Gaussian probability distribution, chaotic sequences, dynamic crowding distance, and self-adaptive mutation operator. Moreover, multiple effective strategies, such as mixed-variable handling approach, constraint handling technique, and stopping criteria, are employed. The effectiveness of the proposed algorithm for solving the ORPD problem is validated on the standard IEEE 30-bus and IEEE 118-bus systems under nominal and contingency states. The obtained results are compared with classical MOPSO, nondominated sorting genetic algorithm (NSGA-II), multiobjective evolutionary algorithm based on decomposition (MOEA/D), and other methods recently reported in the literature from the point of view of Pareto fronts, extreme, solutions and multiobjective performance metrics. The numerical results demonstrate the superiority of the proposed HMOPSO in solving the ORPD problem while strictly satisfying all the constraints.
Funder
Ministry of Science and Technology of China
Subject
Electrical and Electronic Engineering,General Computer Science,Signal Processing
Cited by
15 articles.
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