期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 32, 期 4, 页码 3315-3316出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2016.2631895
关键词
Extreme events; machine learning; power system resilience
资金
- US National Science Foundation [CMMI-1434771]
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [1434771] Funding Source: National Science Foundation
A machine learning based prediction method is proposed in this paper to determine the potential outage of power grid components in response to an imminent hurricane. The decision boundary, which partitions the components' states into two sets of damaged and operational, is obtained via logistic regression by using a second-order function and proper parameter fitting. Two metrics are examined to validate the performance of the obtained decision boundary in efficiently predicting component outages.
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