Publication: Resiliency Assessment in Distribution Networks Using GIS-Based Predictive Risk Analytics
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Undergraduate course
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Ieee-inst Electrical Electronics Engineers Inc
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Article
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Abstract
A new predictive risk-based framework is proposed to increase power distribution network resiliency by improving operator understanding of the status of the grid. This paper expresses the risk assessment as the correlation between likelihood and impact. The likelihood is derived from the combination of Naive Bayes learning and Jenks natural breaks classifier. The analytics included in a geographic information system platform fuse together a massive amount of data from outage recordings and weather historical databases in just one semantic parameter known as failure probability. The financial impact is determined by a time-series-based formulation that supports spatiotemporal data from fault management events and customer interruption cost. Results offer prediction of hourly risk levels and monthly accumulated risk for each feeder section of a distribution network allowing for timely tracking of the operating condition.
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Keywords
Power distribution system, risk assessment, Naive Bayes learning, failure probability, time series, interruption cost, geographic information system (GIS)
Language
English
Citation
Ieee Transactions On Power Systems. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 34, n. 6, p. 4249-4257, 2019.
