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Eigenvalue-based optimal placement of PMUs in large power systems

机译:大型电力系统中基于特征值的PMU的最佳放置

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This paper presents an eigenvalue-based approach for optimal placement of phasor measurement units (PMUs) in large power systems. Eigenvalues of system parameters are used to determine the critical points of the system. The identification of the critical points of a system for PMU installation could be crucial in the detection of potential outages and preventing them from occurring. This method ensures that a minimal number of PMU locations are identified and that the loss of a PMU does not affect the reliability of the monitoring system. Therefore, it is cost-effective. The number of measurements obtainable using this approach is fewer than the 2n-1 required for conventional state estimation to function. (n is the number of buses in the system.) The proposed approach is a holistic approach to the monitoring of the entire system, in that the placement of PMUs for monitoring critical points of the system does not happen at the expense of monitoring the rest of the system. To show that the identified critical PMU measurements are capable of accurately estimating all system variables, an artificial neural network is utilized to map the estimation function between the critical variables and the rest of the system variables. The performance of the proposed techniques is demonstrated on the IEEE 118-bus system.
机译:本文提出了一种基于特征值的方法,用于在大型电力系统中优化相量测量单元(PMU)的位置。系统参数的特征值用于确定系统的临界点。确定用于安装PMU的系统的关键点对于检测潜在的故障并防止发生故障至关重要。此方法可确保识别出最少数量的PMU位置,并且PMU的丢失不会影响监视系统的可靠性。因此,它具有成本效益。使用这种方法可获得的测量数量少于常规状态估计功能所需的2n-1。 (n是系统中的总线数。)提议的方法是监视整个系统的整体方法,因为用于监视系统关键点的PMU的放置不会以监视其余部分为代价系统的。为了显示已识别的关键PMU测量值能够准确估计所有系统变量,使用了人工神经网络在关键变量和其余系统变量之间映射估计函数。 IEEE 118总线系统演示了所提出技术的性能。

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