1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems

Bang Nguyen, Tuyen Vu, Thai-Thanh Nguyen, Mayank Panwar, Rob Hovsapian

Research output: Contribution to conferencePaperpeer-review

Abstract

This paper presents a 1-D convolutional and graph convolutional networks for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme includes fault event detection, fault type and phase classification, and fault location. There are five neural network model training to handle these tasks. Transfer learning and fine-tuning are applied to reduce training efforts. The combined 1-D convolutional and graph convolutional networks (1D-CGCN) is compared with the traditional ANN structure on the Potsdam 13-bus microgrid dataset. The accuracy of 99.5%, 98.4%, 99.2%, and 95.5% are achieved in fault event detection, fault type classification, fault phase identification, and fault location respectively. The detailed confusion matrices of fault type and fault phase classification are provided for validation.

Original languageAmerican English
Number of pages6
DOIs
StatePublished - 2022
Event1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022 - Kharagpur, India
Duration: 9 Dec 202211 Dec 2022

Conference

Conference1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022
Country/TerritoryIndia
CityKharagpur
Period9/12/2211/12/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

NREL Publication Number

  • NREL/CP-5C00-86650

Keywords

  • deep neural network
  • Fault detection
  • fault location
  • graph learning
  • microgrid protection

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