A Multi-Stage Accelerated Quantum Particle Swarm Optimization for Planning and Operation of Static Var Compensators

Manuel S. Alvarez‐Alvarado, D. Jayaweera
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引用次数: 5

Abstract

By the employment of quantum mechanics, this paper proposes a Multi-Stage Accelerated Quantum Particle Swarm Optimization (MSAQPSO) to maximize savings due to electrical power losses reduction, which is subject to bus voltage constraints. The methodology incorporates Static Var Compensators (SVCs) integrated in a power system. The optimization problem is solved using a novel algorithm which consist of two stages. The first stage or the outer layer determines the optimum number, sizing, and placement of the SVCs. The second stage or the inner layer determines the optimum operation of the SVCs. The results reveal that the approach is feasible, and the optimization turns out to be fast and robust in comparison to the classical Particle Swarm Optimization (PSO).
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静态无功补偿器规划与运行的多级加速量子粒子群优化
本文利用量子力学原理,提出了一种多阶段加速量子粒子群优化算法(MSAQPSO),在受母线电压约束的情况下,最大限度地减少电力损耗。该方法将静态无功补偿器(SVCs)集成到电力系统中。提出了一种分两阶段求解优化问题的新算法。第一阶段或外层确定svc的最佳数量、大小和位置。第二阶段即内层决定svc的最佳运行。结果表明,该方法是可行的,与经典粒子群算法相比,优化速度快,鲁棒性好。
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