Arcing Fault Detection with Interpretable Learning Model under the Integration of Renewable Energy

Yousaf Hashmy, Qiushi Cui, Zhihao Ma, Yang Weng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Under the trend of deeper renewable energy integration, active distribution networks are facing increasing uncertainty and security issues, among which the arcing fault detection (AFD) has baffled researchers for years. Existing machine learning based AFD methods are deficient in feature extraction and model interpretability. To overcome these limitations in learning algorithms, we have designed a way to translate the non-transparent machine learning prediction model into an implementable logic for AFD. Moreover, the AFD logic is tested under different fault scenarios and realistic renewable generation data, with the help of our self-developed AFD software. The performance from various tests shows that the interpretable prediction model has high accuracy, dependability, security and speed under the integration of renewable energy.

Original languageEnglish (US)
Title of host publication51st North American Power Symposium, NAPS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728104072
DOIs
StatePublished - Oct 2019
Event51st North American Power Symposium, NAPS 2019 - Wichita, United States
Duration: Oct 13 2019Oct 15 2019

Publication series

Name51st North American Power Symposium, NAPS 2019

Conference

Conference51st North American Power Symposium, NAPS 2019
CountryUnited States
CityWichita
Period10/13/1910/15/19

Keywords

  • Arcing Fault Detection
  • Distribution Networks
  • Power System Protection
  • Renewable Energy

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Hashmy, Y., Cui, Q., Ma, Z., & Weng, Y. (2019). Arcing Fault Detection with Interpretable Learning Model under the Integration of Renewable Energy. In 51st North American Power Symposium, NAPS 2019 [8999972] (51st North American Power Symposium, NAPS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NAPS46351.2019.8999972