Deep Convolutional Neural Networks for Distribution System Fault Classification

Conference · Wed Aug 01 04:00:00 EDT 2018 · 2018 IEEE Power & Energy Society General Meeting (PESGM)

DOI:https://doi.org/10.1109/pesgm.2018.8586547· OSTI ID:1820922

Tian, Guanyu ^[1]; Zhou, Qun; Du, Liang

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1. University of central Florida

Faults happen very frequently in distribution systems. Identifying fault types and phases are of critical importance for outage management, fault location, and service restoration. However, this task becomes very challenging due to measurement scarcity in distribution systems. This paper is among the first few that applies deep learning techniques in distribution system fault classification. Specifically, a sequential Convolutional Neural Network(CNN)-based classifier is developed to identify fault buses and phases. The input to the CNN is the steady-state voltage and current data measured at substations. The fault identification is modeled as a multi-label classification problem. Training data under various fault scenarios are obtained in OpenDSS and Gaussian noises are added to mimic measurement errors. A case study in IEEE 13-feeder test system is conducted with single and multiple bus faults scenarios. Numerical results demonstrate the high accuracy and fast computation of the proposed deep CNN-based fault classification.

• Research Organization: University of central Florida
• Sponsoring Organization: USDOE Office of Artificial Intelligence and Technology (AITO); USDOE
• DOE Contract Number: EE0007998; EE0007327; EE0006340
• OSTI ID: 1820922
• Conference Information: Journal Name: 2018 IEEE Power & Energy Society General Meeting (PESGM)
• Country of Publication: United States
• Language: English