Multi-Objective Reactive Power Optimization Based on Improved Particle Swarm Optimization With ε-Greedy Strategy and Pareto Archive Algorithm

• Published in: IEEE access Vol. 9; pp. 65650 - 65659
• Main Authors: Liu, Xiaofei, Zhang, Pei, Fang, Hui, Zhou, Yinglu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Archives & records; Genetic algorithms; Heuristic algorithms; Iterative methods; Linear programming; Multi-objective reactive power optimization; Multiple objective analysis; Optimization; Pareto archive algorithm; Pareto optimization; Pareto optimum; Particle swarm optimization; Power system stability; Reactive power; Sorting algorithms; voltage control; ε-<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">greedy strategy
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3075777

This paper proposes combining an improved particle swarm optimization and Pareto archive algorithm to solve the multi-objective reactive power optimization problem. The idea of <inline-formula> <tex-math notation="LaTeX">\varepsilon </tex-math></inline-formula> -greedy strategy is adopted and designed to improve particle swarm optimization algorithm. It makes some particles have stronger global search capability, meanwhile, others have stronger local search capability during the whole iteration process. Henceforth, the strategy significantly explores the possibility of optimal solution in local space at the early stage of the iteration, in addition, it mitigates the tendency to fall into the local optimal solution at the later stage of the iteration. The Pareto optimal solution selection problem is solved by minimizing the sum of the difference between each objective function and its optimal solution. The proposed approach is tested on IEEE39-bus and IEEE118-bus system, and it is demonstrated that the proposed approach not only restores the nodes voltage to the normal range and achieves better value for each objective function, but also outperforms other algorithms including standard particle swarm optimization and non-dominated sorting genetic algorithm II(NSGA-II).