Real-time Event Detection Using Rank Signatures of Real-world PMU Data
Real-time Event Detection Using Rank Signatures of Real-world PMU Data
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DOI：
10.1109/pesgm48719.2022.9917156
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Amir Ghasemkhani;Yunchuan Liu;Lei Yang
中科院分区：
文献类型：
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作者：
Amir Ghasemkhani;Yunchuan Liu;Lei Yang
电力系统事件的及时检测是一项至关重要的任务，它可以促进补救措施的实施，以提高系统的可靠性，弹性和安全性。同时，相量测量单元（PMU）的广泛部署使得开发数据驱动的事件检测技术成为可能。然而，纯粹依赖于数据而不结合领域知识的电力系统中的事件检测任务构成了重大的安全和稳定性风险，由于与数据误解和模型准确性相关的问题。在这方面，我们提出了一个实时的事件检测方法，使用真实世界的PMU数据，结合领域知识，充分捕捉事件签名。具体来说，我们跟踪PMU数据的秩签名的变化，以准确地定位事件。为了优化检测过程，我们采用了离线贝叶斯优化算法，通过有效地搜索最佳值来调整参数。实验结果表明，该方法能够有效地检测出PMU数据流中的事件，并具有较高的检测精度。
Timely detection of power system events is a crucial task, which can facilitate the implementation of remedial actions to improve reliability, resiliency, and security of the system. Meanwhile, the widespread deployment of phasor measurement units (PMUs) makes it possible to develop data-driven event detection techniques. However, relying purely on data without incorporating domain knowledge for the event detection task in power systems poses substantial security and stability risks due to issues associated with data misinterpretation and model accuracy. In this regard, we propose a real-time event detection method using real-world PMU data by incorporating domain knowledge to adequately capture the event signatures. Specifically, we track the change in rank signatures of PMU data to accurately localize the events. To optimize the detection process, we incorporate an offline Bayesian optimization algorithm to tune the parameters by efficiently searching for the best values. The experiments using the real-world PMU dataset from a U.S. interconnection show that the proposed event detection approach can efficiently detect the events from PMU data streams with high accuracy.