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Principal components fault location based on WAMS/PMU measure system

机译:基于WAMS / PMU测量系统的主成分故障定位

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摘要

In this paper, a novel fault location method based on Principal Component Analysis (PCA) is proposed, in which synchronized information data are utilized provided by the WAMS/PMU measurement system. PCA method is an efficient mathematic tool to reduce dimensions. Therefore, the state matrix, consisting of raw synchrophasor information versus multi-points and various times, is compressed effectively. By the remained key features, the corresponding principal component scores could be obtained. The simulation results have shown that, according to these scores, the fault component can be confirmed exactly. A great number of simulation trials have fully proved that the results of principal component analysis are accurate and reliable.
机译:本文提出了一种基于主成分分析(PCA)的故障定位新方法,该方法利用了WAMS / PMU测量系统提供的同步信息数据。 PCA方法是减少尺寸的有效数学工具。因此,有效地压缩了由原始同步相量信息与多点和不同时间组成的状态矩阵。通过其余的关键特征,可以获得相应的主成分评分。仿真结果表明,根据这些分数,可以准确地确定故障分量。大量的模拟试验已经充分证明了主成分分析的结果是准确可靠的。

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