Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor (Journal Article) | OSTI.GOV
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Title: Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor

Journal Article · · IEEE Transactions on Smart Grid

With the deployment of phasor measurement units (PMU) and wide area measurement system (WAMS), it is feasible to have an insight into the events occurred in power systems based on measured data. Thus, a novel data-driven algorithm based on local outlier factor (LOF) is proposed in this work to detect and locate events in power systems using reduced PMU data. First, the unequal-interval reduction method is presented to reduce the scale of PMU data in sub-stations and reconstruct it in master station of WAMS, which can relieve the burden of communication systems. Then, principle component analysis (PCA)-based similarity search method is proposed to measure the differences of operation state between any two buses. Next, LOF is presented to detect the abnormal events in power systems, and employed to determine the region of the event source. Finally, six cases from the Western electricity coordinating council (WECC) 179-bus power system, a case from the South China power system (SCPS), and a case from the Guangdong power system (GDPS) are utilized to demonstrate the effectiveness of the proposed algorithm. Overall, the results show that proposed algorithm is effective and can be applied to event detection, event location, and online monitoring, which can enhance the situation awareness ability of power system operators.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1661203
Alternate ID(s):
OSTI ID: 1665971
Journal Information:
IEEE Transactions on Smart Grid, Vol. 11, Issue 2; ISSN 1949-3053
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 61 works
Citation information provided by
Web of Science