Automatic Power System Event Classification Using Quadratic Discriminant Analysis on PMU Data
Automatic Power System Event Classification Using Quadratic Discriminant Analysis on PMU Data
复制标题
使用 PMU 数据的二次判别分析进行自动电力系统事件分类
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
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通讯作者：
X. Liu
中科院分区：
文献类型：
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作者：
M. Rafferty;X. Liu
电力系统广域监测中的事件快速检测和诊断是系统运行人员非常感兴趣的问题，事件分类是事件诊断的一个主要方面。其他事件诊断方面包括事件发生的时间、事件的位置、事件的根本原因和事件的严重程度。自动事件分类增强了操作员快速识别系统中发生的事件类型的能力，这有助于在恢复系统供电时快速做出决策。提出了一种基于相量测量单元（PMU）数据的二次判别分析（QDA）和前向选择技术的电力系统事件分类方法。QDA是一种常用的数据分类监督统计技术，其工作原理是通过对数据之间的差异进行建模，找到将数据分为不同类别的特征组合。历史电力系统事件数据用于构建事件数据库，并且当检测到新事件时，该方法基于对电力系统变量的影响自动地对事件进行分类。所提出的方法的可靠性证明使用模拟的案例研究，构建使用DigSilent Power Factory，和真实的数据案例研究，从英国和爱尔兰电力系统。
Rapid detection and diagnosis of events in power system wide area monitoring is of great interest to system operators, with event classification being a major aspect of diagnosing an event. Other event diagnostic aspects include the time the event occurred, location of the event, root cause of the event and magnitude of the event. Automatic event classification enhances the operators’ ability to identify the types of events occurring in a system quickly, which helps to assist fast decision making when restoring power to the system. This paper proposes an approach for classifying power system events, namely Generation Dip, Loss of Load and Line Trip Events, by employing Quadratic Discriminant Analysis (QDA) on Phasor Measurement Unit (PMU) data in combination with a forward selection technique. QDA is a commonly used supervised, statistical technique for data classification, and works by finding a combination of features that separates the data into different classes by modelling the difference between them. Historical power system event data is used to construct an event database, and as new events are detected the methodology automatically classifies the event based on the effect on power system variables. The reliability of the proposed method is demonstrated using simulated case studies, constructed using DigSilent Power Factory, and real data case studies, acquired from the UK and Irish Power System.