Artificial Intelligence based Optimal Placement of Phasor Measurement Unit for Smart Grid

Sushmita Poudel, S. Adhikari
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Abstract

A Phasor Measurement Unit (PMU) device ensures the stability, reliability, and proper visibility of critical areas of the power grid by providing a synchro phasor measurement of both voltage and current in real-time. Since it is not practical to place them at each branch of the system, the main concern lies in achieving entire network observability either by direct or indirect measurement through an optimal set of PMUs placed at the optimal location. This gives rise to the Optimal PMU Placement Problem (OPP). In this paper, OPP is solved by an optimization technique i.e., a Genetic Algorithm. The constraints that can greatly affect obtaining optimal solutions are reviewed and the effect of including Zero Injection Bus (ZIB) on the proposed method in solving OPP is studied. The algorithm is applied to IEEE-14 and IEEE-30 bus test systems for validation purposes. Since optimal sets of solutions are obtained from the proposed method, the solutions are ranked by the system observability redundancy index to find the best one. Further, a comparative analysis of the result is done with the depth first search algorithm and other widely used algorithms.
基于人工智能的智能电网相量测量单元优化配置
相量测量单元(PMU)设备通过实时同步测量电压和电流,确保电网关键区域的稳定性、可靠性和适当的可视性。由于将它们放置在系统的每个分支是不实际的,因此主要关注的是通过放置在最佳位置的一组最优pmu,通过直接或间接测量来实现整个网络的可观察性。这就产生了最优PMU放置问题(OPP)。本文采用一种优化技术,即遗传算法来求解OPP问题。回顾了对求解最优解有很大影响的约束条件,并研究了零注入总线(Zero Injection Bus, ZIB)对所提方法求解OPP的影响。将该算法应用于IEEE-14和IEEE-30总线测试系统进行验证。由于该方法可获得最优解集,因此根据系统可观察性冗余度指标对解进行排序,以找到最优解。此外,还将深度优先搜索算法与其他常用算法进行了对比分析。
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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