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首页> 外文期刊>International Journal of Electrical and Electronic Science >New Hybrid Particle Swarm Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem
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New Hybrid Particle Swarm Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem

机译:解决最优无功调度问题的新型混合粒子群算法

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This paper presents a hybrid particle swarm algorithm for solving the multi-objective reactive power dispatch problem. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optimization problem in various science and engineering domains. The standard Particle Swarm Optimization (PSO) algorithm is a novel evolutionary algorithm in which each particle studies its own previous best solution and the group’s previous best to optimize problems. One problem exists in PSO is its tendency of trapping into local optima. This paper proposes a hybrid approach by combining a Euclidian distance (EU) based genetic algorithm (GA) and particle swarm optimization (PSO) method - New hybrid particle swarm optimization (NHPSO). The simulation results demonstrate good performance of the NHPSO in solving an optimal reactive power dispatch problem. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms reported those before in literature. Results show that NHPSO is more efficient than others for solution of single-objective optimal reactive power dispatch (ORPD) problem.
机译:提出了一种求解多目标无功调度问题的混合粒子群算法。系统的模态分析用于静态电压稳定性评估。目标是使损耗最小化和使电压稳定裕度最大化。发电机端子电压,电容器组的无功功率和抽头变换变压器设置被用作优化变量。进化算法和Swarm Intelligence算法(EA,SI)是Bio启发式优化算法的一部分,已广泛用于解决各个科学和工程领域的众多优化问题。标准粒子群优化(PSO)算法是一种新颖的进化算法,其中,每个粒子都研究自己的先前最佳解决方案,并研究该组先前的最佳解决方案。 PSO中存在的一个问题是其陷入局部最优状态的趋势。本文提出了一种基于欧氏距离(EU)的遗传算法(GA)和粒子群优化(PSO)方法相结合的混合方法-新型混合粒子群优化(NHPSO)。仿真结果证明了NHPSO在解决最优无功调度问题上的良好性能。为了评估该算法,该算法已经在IEEE 30总线系统上进行了测试,并与文献中以前报道的其他算法进行了比较。结果表明,NHPSO在解决单目标最优无功功率分配(ORPD)问题上比其他方法更有效率。

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