A Machine Learning Approach for Line Outage Identification in Power Systems
A Machine Learning Approach for Line Outage Identification in Power Systems
复制标题
用于电力系统线路停电识别的机器学习方法
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
M. Crow
中科院分区：
文献类型：
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作者：
Jia He;M. Cheng;Yixin Fang;M. Crow
提出了一种仅利用测量数据进行电力线拓扑变化检测的方法。随着相量测量单元（PMU）的广泛部署，电力系统监测和实时分析可以利用PMU提供的大量数据，并利用大数据分析的进步。在本文中，我们开发了实用的分析，不紧密耦合的潮流分析和状态估计，因为这些任务需要详细和准确的信息，电力系统。我们专注于电力线路停电识别，并使用机器学习框架来定位停电。相同的框架用于单线路停电识别和多线路停电识别。该方法首先计算电力系统拓扑变化时的动态特征，然后将时域数据转换为频域数据，并基于频域特征训练算法进行停电预测。所提出的方法只使用电压相量角连续监测总线。所提出的方法是由模拟PMU数据PSAT [1]进行测试，预测精度与以前的工作，涉及解决潮流方程或状态估计方程。
This paper addresses power line topology change detection by using only measurement data. As Phasor Measurement Units (PMUs) become widely deployed, power system monitoring and real-time analysis can take advantage of the large amount of data provided by PMUs and leverage the advances in big data analytics. In this paper, we develop practical analytics that are not tightly coupled with the power flow analysis and state estimation, as these tasks require detailed and accurate information about the power system. We focus on power line outage identification, and use a machine learning framework to locate the outage(s). The same framework is used for both single line outage identification and multiple line outage identification. We first compute the features that are essential to capture the dynamic characteristics of the power system when the topology change happens, transform the time-domain data to frequency-domain, and then train the algorithms for the prediction of line outage based on frequency domain features. The proposed method uses only voltage phasor angles obtained by continuous monitoring of buses. The proposed method is tested by simulated PMU data from PSAT [1], and the prediction accuracy is comparable to the previous work that involves solving power flow equations or state estimation equations.