AMI-enabled Distribution Network Line Outage Identification via Multi-label SVM

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Daniel Felix Ritchie School of Engineering and Computer Science, Electrical and Computer Engineering


Kernel, Support vector machines, Smart meters, Power system reliability, Companies, Meteorology, Network topology


This letter proposes an effective data mining method for identifying distribution network line outages by leveraging data collected through Advanced Metering Infrastructure (AMI). The line outage identification method is developed based on a Multi-Label Support Vector Machine (ML-SVM) classification scheme that utilizes the status of customers' smart meters as input data and accordingly identifies the outage/operational status of distribution lines. The F β -score is proposed to validate the performance of the classifier through numerical simulations.

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