Optimal location and setting of SVC and TCSC devices using non-dominated sorting particle swarm optimization
Optimal location and setting of SVC and TCSC devices using non-dominated sorting particle swarm optimization
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DOI：
10.1016/j.epsr.2009.07.004
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发表时间：
2009-12-01
影响因子：
3.9
通讯作者：
Abido, M. A.
中科院分区：
文献类型：
--
作者：
Benabid, R.;Boudour, M.;Abido, M. A.
本文提出了一种优化定位多类型 FACTS 设备以优化多目标电压稳定性问题的新方法。所提出的方法基于粒子群优化 (PSO) 的新变体，专门用于多目标优化问题，称为非支配排序粒子群优化 (NSPSO)。拥挤距离技术用于将帕累托前沿尺寸维持在选定的极限，而不破坏其特性。为了帮助决策者从帕累托前沿选择最佳折衷解决方案，这项任务采用了基于模糊的机制。 NSPSO 用于寻找两种 FACTS 的最佳位置和设置，即晶闸管控制串联补偿器 (TCSC) 和静态无功补偿器 (SVC)，以最大限度地提高静态电压稳定裕度 (SVSM)、降低有功功率损耗 (RPL) 和负载电压偏差 (LVD)。考虑到各种FACTS组合，对两个和三个目标函数进行优化。为了保证该方法的鲁棒性并赋予我们研究的实际意义，在优化过程中考虑了N-1列联分析和电力系统的应力。线路的热限制和负载总线的电压限制被视为安全约束。所提出的方法在 IEEE 30 总线和现实的阿尔及利亚 114 总线电力系统上得到了验证。仿真结果与粒子群优化（PSO）和非支配排序遗传算法（NSGA-II）获得的结果进行了比较。比较显示了所提出的 NSPSO 解决多目标优化问题的有效性，并捕获具有令人满意的多样性特征的 Pareto 最优解。 (c) 2009 Elsevier B.V. 保留所有权利。
In this paper, a new method for optimal locating multi-type FACTS devices in order to optimize multi-objective voltage stability problem is presented. The proposed methodology is based on a new variant of particle swarm optimization (PSO) specialized in multi-objective optimization problem known as non-dominated sorting particle swarm optimization (NSPSO). The crowding distance technique is used to maintain the Pareto front size at the chosen limit, without destroying its characteristics. To aid the decision maker choosing the best compromise solution from the Pareto front, the fuzzy-based mechanism is employed for this task. NSPSO is used to find the optimal location and setting of two types of FACTS namely: Thyristor controlled series compensator (TCSC) and static var compensator (SVC) that maximize static voltage stability margin (SVSM), reduce real power losses (RPL), and load voltage deviation (LVD). The optimization is carried out on two and three objective functions for various FACTS combinations considering. For ensure the robustness of the proposed method and gives a practical sense of our study, N-1 contingency analysis and the stress of power system is considered in the optimization process. The thermal limits of lines and voltage limits of load buses are considered as the security constraints. The proposed method is validated on IEEE 30-bus and realistic Algerian 114-bus power system. The simulation results are compared with those obtained by particle swarm optimization (PSO) and non-dominated sorting genetic algorithms (NSGA-II). The comparisons show the effectiveness of the proposed NSPSO to solve the multi-objective optimization problem and capture Pareto optimal solutions with satisfactory diversity characteristics. (c) 2009 Elsevier B.V. All rights reserved.