N-1 Based Optimal Placement of SVC Using Elitist Genetic Algorithm in Terms of Multiobjective Problem Statement

V. Popovtsev, D. Ignatiev, D. Snegirev
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引用次数: 1

Abstract

Processes of new reactive power compensation devices (RPCD) installation or replacement of old ones are becoming more relevant due to the development of power electronics. An emphasis is made on more efficient and flexible FACTS (Flexible Alternating Current Transmission System) devices - mainly static var compensators (SVC) and static synchronous compensators (STATCOM). In addition, it is necessary to solve a number of problems related to the search for the optimal location of an RPCD and the selection of its parameters. It should be carried out for the purpose of achieving the highest technical and economic effect for a power system as a whole or its region. The multiobjective optimization (MOO) methods, particularly heuristic ones (genetic algorithms, particle swarm methods, simulated annealing etc.), are proven to be efficient in the analysis of power systems planning. The drawbacks of these methods are well known, namely their dependency on forms of objective functions. The importance of taking N-1 criterion into account in questions of optimal placement of an SVC was shown in the article. As a consequence, the security indices were suggested to use as the additional objective functions. Moreover, the range of probabilistic parameters is not limited to expected values of the objective functions components but also it includes moments of higher orders. The test results have shown that the forms of objective functions and plenty of the factors taken into account (e.g. post-contingency states) significantly influence the problem solution. It was demonstrated that the optimization results possess great degree of uncertainty in the case of large number of criteria for the selection of a bus for an RPCD.
多目标问题表述中基于N-1的SVC优化配置的精英遗传算法
随着电力电子技术的发展,新的无功补偿装置(RPCD)的安装或旧装置的更换过程变得越来越重要。重点是更高效和灵活的FACTS(柔性交流传输系统)设备-主要是静态无功补偿器(SVC)和静态同步补偿器(STATCOM)。此外,还需要解决一些与RPCD最佳位置的寻找和参数的选择有关的问题。应以实现电力系统整体或区域的最高技术经济效益为目的。多目标优化(MOO)方法,特别是启发式方法(遗传算法、粒子群方法、模拟退火等)在电力系统规划分析中被证明是有效的。这些方法的缺点是众所周知的,即它们依赖于目标函数的形式。考虑N-1标准在SVC的最优放置问题的重要性在文章中显示。因此,建议使用安全指标作为附加目标函数。此外,概率参数的范围不仅限于目标函数分量的期望值,还包括高阶矩。测试结果表明,目标函数的形式和考虑的大量因素(如后事件状态)对问题的解决有显著影响。结果表明,在RPCD客车选择标准较多的情况下,优化结果具有很大的不确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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