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Hybrid multi-swarm particle swarm optimisation based multi-objective reactive power dispatch

机译:基于混合多群粒子群算法的多目标无功调度

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摘要

Most of the real-world optimisation problems are subject to different types of constraints and are known as constrained optimisation problems. Reactive power dispatch (RPD) in electrical power system is also a non-linear, multi-objective or a single objective constrained optimisation problem. In this study, hybrid multi-swarm particle swarm optimisation (HMPSO) algorithm has been proposed to solve the RPD problem. HMPSO is one of the recently proposed population based search algorithm, in which the existing swarm is partitioned into several sub-swarms. Particle swarm optimisation is applied as the search engine for each sub-swarm. In addition, to explore more promising regions of the search space, differential evolution (DE) algorithm is implemented to improve the personal best of each particle. The RPD problem is formulated as non-linear, constrained multi-objective optimisation problem with equality and inequality constraints for minimisation of power losses and improvement of voltage profile simultaneously. To find the Pareto optimal set for RPD problem, weighted sum method has been applied. Afterwards, for finding the preferred solution out of the Pareto-optimal set, fuzzy membership function has been used. Effectiveness of the HMPSO algorithm has been verified on the standard IEEE 30-bus and a practical 75-bus Indian systems.
机译:现实世界中的大多数优化问题都受到不同类型的约束,并且被称为约束优化问题。电力系统中的无功功率分配(RPD)也是一个非线性,多目标或单目标约束的优化问题。在这项研究中,提出了混合多群粒子群优化算法(HMPSO)来解决RPD问题。 HMPSO是最近提出的基于种群的搜索算法之一,该算法将现有的群体划分为几个子群体。应用粒子群优化作为每个子群的搜索引擎。此外,为了探索搜索空间中更有希望的区域,还实施了差分进化(DE)算法,以提高每个粒子的最佳表现。 RPD问题被公式化为具有相等和不平等约束的非线性约束多目标优化问题,以最大程度地降低功率损耗并同时改善电压曲线。为了找到RPD问题的帕累托最优集,已应用加权和方法。之后,为了从帕累托最优集合中找到首选解决方案,使用了模糊隶属度函数。 HMPSO算法的有效性已在标准IEEE 30总线和实用的75总线印度系统上得到验证。

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