Power grid operators assess situational awareness using time-tagged measurements from phasor measurement units (PMUs) placed at multiple locations in a network. However, synchrophasor measurements are prone to anomalies which may impact the performance of phasor based applications. Anomalies include any deviation from expected measurements resulting from power system events or bad data. Bad data include data errors or loss of information due to failures in supporting synchrophasor cyber infrastructure. It is necessary to flag bad data before utilizing for an application. This work proposes a tool for the detection and classification of anomalous data using an unsupervised stacked ensemble learning algorithm. The proposed synchrophasor anomaly detection and classification (SyADC) tool analyzes a selected window of data points using a combination of three unsupervised methods, namely: isolation forest, KMeans and LoOP. The method classifies the data as anomalies or normal data with more than 99% recall. The method also provides a probability of the data to be an event or bad data with more than 99% recall. Results for the IEEE 14 and 68 bus systems with synchrophasor data obtained using Real-Time Digital Simulator and data of industrial PMUs highlight the superiority of the algorithm to detect and classify anomalies.

中文翻译：

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实时同步相量数据异常检测与分类 隔离林，KMeans和LoOP

电网运营商使用来自网络中多个位置的相量测量单元（PMU）的带时标的测量来评估态势感知。但是，同步相量测量容易出现异常，这可能会影响基于相量的应用程序的性能。异常包括由于电力系统事件或不良数据而导致的与预期测量的任何偏差。不良数据包括由于支持同步相量网络基础架构失败而导致的数据错误或信息丢失。在用于应用程序之前，有必要标记不良数据。这项工作提出了一种使用无人监督的集成学习算法对异常数据进行检测和分类的工具。拟议的同步相量异常检测和分类（SyADC）工具使用三种无监督方法（隔离林，KMeans和LoOP）的组合来分析数据点的选定窗口。该方法将数据分类为异常或正常数据，召回率超过99％。该方法还提供了数据成为事件或不良数据的可能性，召回率超过99％。使用实时数字仿真器获得的具有同步相量数据的IEEE 14和68总线系统的结果以及工业PMU的数据突显了该算法检测和分类异常的优越性。该方法还提供了数据成为事件或不良数据的可能性，召回率超过99％。使用实时数字仿真器获得的具有同步相量数据的IEEE 14和68总线系统的结果以及工业PMU的数据突显了该算法检测和分类异常的优越性。该方法还提供了数据成为事件或不良数据的可能性，召回率超过99％。使用实时数字仿真器获得的具有同步相量数据的IEEE 14和68总线系统的结果以及工业PMU的数据突显了该算法检测和分类异常的优越性。