A PMU-based Multivariate Model for Classifying Power System Events
A PMU-based Multivariate Model for Classifying Power System Events
复制标题
基于 PMU 的电力系统事件分类多元模型
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Sara Eftekharnejad
中科院分区：
文献类型：
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作者：
Rui Ma;S. Basumallik;Sara Eftekharnejad
实时暂态事件识别是电力系统态势感知与保护的关键。相量测量单元（pmu）的普及增强了电力系统的可视化和实时监测与控制。然而，针对pmu的恶意虚假数据注入攻击可能会提供错误的数据，从而可能促使操作人员采取错误的操作，从而最终危及系统的可靠性。本文通过分析PMU时间序列的属性及其相互关系，提出了一种基于文本挖掘的多变量方法来检测虚假数据和识别瞬态事件。结果表明，该方法能够有效地检测出错误数据，并识别出各个暂态事件，而不受系统拓扑结构和负载条件以及pmu的覆盖率和位置的影响。该方法在IEEE 30总线系统上进行了测试，并给出了分类结果。
Real-time transient event identification is essential for power system situational awareness and protection. The increased penetration of Phasor Measurement Units (PMUs) enhance power system visualization and real time monitoring and control. However, a malicious false data injection attack on PMUs can provide wrong data that might prompt the operator to take incorrect actions which can eventually jeopardize system reliability. In this paper, a multivariate method based on text mining is applied to detect false data and identify transient events by analyzing the attributes of each individual PMU time series and their relationship. It is shown that the proposed approach is efficient in detecting false data and identifying each transient event regardless of the system topology and loading condition as well as the coverage rate and placement of PMUs. The proposed method is tested on IEEE 30-bus system and the classification results are provided.