利用相量测量单元进行低分辨率故障定位
Low-Resolution Fault Localization Using Phasor Measurement Units with Community Detection
AI总结：
本文研究了在测量有限的情况下，如何利用图聚类概念通过子图级别定位不同类型的配电线路故障，突破可观测性限制。
摘要：
大量文献在故障定位中假设系统有足够的可靠测量保证可观测性。尽管存在几种启发式方法突破可观测性障碍，但它们主要依赖于识别时空模式，而不提供性能与系统特性及传感器部署之间的联系。本文旨在填补这一空白，研究在低测量情况下利用相量测量单元（PMUs）进行故障定位的限制和性能极限。我们的主要贡献是展示如何利用有限的测量数据在子图级别定位不同类型配电线路故障（三相、单相接地等），而不是在线路级别。我们证明所获得的分辨率与网络科学中的图聚类概念密切相关。
英文摘要：
A significant portion of the literature on fault localization assumes (more or less explicitly) that there are sufficient reliable measurements to guarantee that the system is observable. While several heuristics exist to break the observability barrier, they mostly rely on recognizing spatio-temporal patterns, without giving insights on how the performance are tied with the system features and the sensor deployment. In this paper, we try to fill this gap and investigate the limitations and performance limits of fault localization using Phasor Measurement Units (PMUs), in the low measurements regime, i.e., when the system is unobservable with the measurements available. Our main contribution is to show how one can leverage the scarce measurements to localize different type of distribution line faults (three-phase, single-phase to ground, ...) at the level of sub-graph, rather than with the resolution of a line. We show that the resolution we obtain is strongly tied with the graph clustering notion in network science.
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