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Optimization of Phasor Measurement Unit (PMU) Placement in Supervisory Control and Data Acquisition (SCADA)-Based Power System for Better State-Estimation Performance

机译:定量测量单元(PMU)放置在监督控制和数据采集(SCADA)的电力系统中的优化,以获得更好的状态估计性能

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摘要

Present-day power systems are mostly equipped with conventional meters and intended for the installation of highly accurate phasor measurement units (PMUs) to ensure better protection, monitoring and control of the network. PMU is a deliberate choice due to its unique capacity in providing accurate phasor readings of bus voltages and currents. However, due to the high expense and a requirement for communication facilities, the installation of a limited number of PMUs in a network is common practice. This paper presents an optimal approach to selecting the locations of PMUs to be installed with the objective of ensuring maximum accuracy of the state estimation (SE). The optimization technique ensures that the critical locations of the system will be covered by PMU meters which lower the negative impact of bad data on state-estimation performance. One of the well-known intelligent optimization techniques, the genetic algorithm (GA), is used to search for the optimal set of PMUs. The proposed technique is compared with a heuristic approach of PMU placement. The weighted least square (WLS), with a modified Jacobian to deal with the phasor quantities, is used to compute the estimation accuracy. IEEE 30-bus and 118-bus systems are used to demonstrate the suggested technique.
机译:现今的电源系统主要配备了传统仪表,旨在安装高精度的相量测量单元(PMU),以确保更好地保护,监控和控制网络。 PMU是一个刻意的选择,因为它在提供准确的总线电压和电流的准确量相读数方面的独特能力。但是,由于高费用和通信设施的要求,在网络中安装有限数量的PMU是常见的做法。本文介绍了选择要安装PMU的位置的最佳方法,以确保状态估计的最大精度(SE)。优化技术确保系统的关键位置将由PMU仪表覆盖,从而降低了不良数据对状态估计性能的负面影响。众所周知的智能优化技术,遗传算法(GA),用于搜索最佳的PMU集。该提出的技术与PMU放置的启发式方法进行了比较。使用修改后的雅可族人处理量量的加权最小二乘(WLS)用于计算估计精度。 IEEE 30-Bus和118总线系统用于展示所提出的技术。

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