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Optimal location and setting of SVC and TCSC devices using non-dominated sorting particle swarm optimization

机译：使用非支配排序粒子群算法优化SVC和TCSC设备的位置和设置

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摘要

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.
机译：为优化多目标电压稳定问题，本文提出了一种优化多类型FACTS装置定位的新方法。所提出的方法基于专门针对多目标优化问题的粒子群优化（PSO）的新变体，称为非支配排序粒子群优化（NSPSO）。拥挤距离技术用于在不破坏其特征的情况下将帕累托的前端尺寸维持在选定的极限。为了帮助决策者从Pareto方面选择最佳折衷解决方案，该任务采用了基于模糊的机制。 NSPSO用于找到两种类型的FACTS的最佳位置和设置：晶闸管控制的串联补偿器（TCSC）和静态无功补偿器（SVC），它们可最大化静态电压稳定裕度（SVSM），减少有功功率损耗（RPL）和负载电压偏差（LVD）。针对各种FACTS组合，在两个和三个目标函数上进行了优化。为了确保所提方法的鲁棒性并为我们的研究提供实践意义，在优化过程中考虑了N-1权变分析和电力系统的压力。线路的热极限和负载母线的电压极限被视为安全约束。该方法在IEEE 30总线和实际的阿尔及利亚114总线电源系统上得到了验证。将模拟结果与通过粒子群优化（PSO）和非主导排序遗传算法（NSGA-II）获得的结果进行比较。比较结果表明，所提出的NSPSO解决多目标优化问题并捕获具有令人满意的多样性特征的Pareto最优解的有效性。

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