Synchronized phasor measurement unit (PMU) data contain rich information about power systems, hence the quality of estimates is of utmost concern. The existing methodologies for estimation rely on certain assumptions regarding the error in the measurement data. This paper revisits a key assumption specifically the Gaussian character of the error. A quantification of the PMU error yields its nature and statistical properties including its dependence on various sections of the PMU instrumentation channel (supposedly the major source of error in the PMU data). The non Gaussian nature of the error is asserted using various null hypotheses tests and a novel Gaussian mixture model based clustering technique is proposed to characterize and relate the errors present in PMU measurement data to the saturation in current transformer, cable length and the PMU burden. The proposed approach is tested using both real and synthetic PMU datasets. The ultimate goal of the paper is towards creating a PMU error emulator for testing and research of data analytic algorithms focused on crucial WAMS based applications.

中文翻译：

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用于基于 WAMS 的稳健分析的 PMU 误差的统计特征

同步相量测量单元 (PMU) 数据包含有关电力系统的丰富信息，因此估计的质量至关重要。现有的估计方法依赖于关于测量数据误差的某些假设。本文重新审视了一个关键假设，特别是误差的高斯特征。PMU 误差的量化产生了它的性质和统计特性，包括它对 PMU 仪器通道的各个部分的依赖（据说是 PMU 数据中误差的主要来源）。使用各种零假设测试来断言误差的非高斯性质，并提出了一种基于高斯混合模型的新型聚类技术，以表征 PMU 测量数据中存在的误差并将其与电流互感器中的饱和度相关联，电缆长度和 PMU 负担。所提出的方法使用真实和合成 PMU 数据集进行了测试。本文的最终目标是创建一个 PMU 错误模拟器，用于测试和研究数据分析算法，重点是基于 WAMS 的关键应用程序。