Realistic Synchrophasor Data Generation for Anomaly Detection and Event Classification
Realistic Synchrophasor Data Generation for Anomaly Detection and Event Classification
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DOI：
10.1109/mscpes49613.2020.9133691
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
K. S. Sajan;M. Bariya;S. Basak;A. Srivastava;A. Dubey;A. von Meier;G. Biswas
中科院分区：
文献类型：
--
作者：
K. S. Sajan;M. Bariya;S. Basak;A. Srivastava;A. Dubey;A. von Meier;G. Biswas
电网自动化和可重构的推动力导致了相量测量单元（PMU）的广泛安装，以改善实时广域系统监测和控制。然而，将大量高分辨率PMU测量转化为可操作的见解仍然具有挑战性。一个核心挑战是在PMU数据流中创建灵活且可扩展的在线异常检测。PMU数据可以保存物理系统或网络系统（测量和通信网络）中出现的多种类型的异常。提高对噪声测量数据和坏数据（BD）异常的电网态势感知变得越来越重要。许多机器学习，数据分析和基于物理的算法已经被开发用于异常检测，但需要用实际的同步相数据进行验证。由于保密和安全原因，获取实地数据非常困难。本文提出了一种为给定的合成网络生成真实的同步相量数据的方法，以及事件和不良数据的检测和分类算法。所开发的算法包括贝叶斯和变点技术，以确定异常，事件本地化和多步聚类方法的事件分类的统计方法。已开发的算法进行了验证与电力系统事件，包括故障和负载/发电机/电容器的变化/切换的IEEE测试系统的多个例子，令人满意的结果。一组同步相量数据将公开提供给其他研究人员。
The push to automate and digitize the electric grid has led to widespread installation of Phasor Measurement Units (PMUs) for improved real-time wide-area system monitoring and control. Nevertheless, transforming large volumes of high-resolution PMU measurements into actionable insights remains challenging. A central challenge is creating flexible and scalable online anomaly detection in PMU data streams. PMU data can hold multiple types of anomalies arising in the physical system or the cyber system (measurements and communication networks). Increasing the grid situational awareness for noisy measurement data and Bad Data (BD) anomalies has become more and more significant. Number of machine learning, data analytics and physics based algorithms have been developed for anomaly detection, but need to be validated with realistic synchophasor data. Access to field data is very challenging due to confidentiality and security reasons. This paper presents a method for generating realistic synchrophasor data for the given synthetic network as well as event and bad data detection and classification algorithms. The developed algorithms include Bayesian and change-point techniques to identify anomalies, a statistical approach for event localization and multi-step clustering approach for event classification. Developed algorithms have been validated with satisfactory results for multiple examples of power system events including faults and load/generator/capacitor variations/switching for an IEEE test system. Set of synchrophasor data will be available publicly for other researchers.