Multi-Task Logistic Low-Ranked Dirty Model for Fault Detection in Power Distribution System

Abstract

This paper proposes a Multi-task Logistic Low-Ranked Dirty Model (MT-LLRDM) for fault detection in power distribution networks by using the distribution Phasor Measurement Unit (PMU) data. The MT-LLRDM improves the fault detection accuracy by utilizing the similarities in the fault data streams among multiple locations across a power distribution network. The captured similarities supplement the information to the task of fault detection at a location of interest, creating a multi-task learning framework and thereby improving the learning accuracy. The algorithm is validated with real-time PMU streams from a hardware-in-the-loop testbed that emulates real field communication and monitoring conditions in distribution networks. The results showed that the MT-LLRDM outperforms other state-of-the-art classification methods using actual synchrophasor data achieved from a power hardware-in-the-loop testbed.