期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 35, 期 4, 页码 2748-2758出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2020.2968611
关键词
Phasor measurement units; Detectors; Microsoft Windows; Anomaly detection; Resilience; Maximum likelihood estimation; Tools; Resiliency; controlled islanding; reconfiguration; resiliency metric; data-quality; RTDS; D-PMU; PMU
资金
- US Department of Energy UI-ASSIST project
- US Department of Energy RADIANCE project
- US Department of Energy AGGREGATE project
- Idaho Falls Power
Measuring and enabling resiliency of electric distribution systems with increasing weather and cyber events are important. Some of the extreme events (e.g. Earthquakes, Hurricanes) and associated paths are predicted and monitored closely in advance and allow to take pre-event proactive control actions. The Distribution Phasor Measurement Units (D-PMUs) provide new opportunities and supporting such proactive actions. A synchrophasor based resiliency driven pre-event reconfiguration can ensure minimizing impact of the expected event on the power distribution system and associated performance. However, the D-PMUs will also face challenges in terms of data quality similar to the transmission PMUs. The focus of this paper is to provide data mining approaches for anomaly detection in D-PMUs and proposing resiliency-driven pre-event reconfiguration with islanding as a proactive mechanisms to minimize the impact of adverse events on system using processed synchrophasors data. Results are validated for real industrial feeders and test cases with satisfactory response.
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