Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
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Identifying Process Capability Index for Electricity Distribution System through Thermal Image Analysis

열화상 이미지 분석을 통한 배전 설비 공정능력지수 감지 시스템 개발

  • Lee, Hyung-Geun (Department of Industrial Engineering, INHA University) ;
  • Hong, Yong-Min (Department of Industrial Engineering, INHA University) ;
  • Kang, Sung-Woo (Department of Industrial Engineering, INHA University)
  • 이형근 (인하대학교 산업경영공학과) ;
  • 홍용민 (인하대학교 산업경영공학과) ;
  • 강성우 (인하대학교 산업경영공학과)
  • Received : 2021.08.05
  • Accepted : 2021.09.08
  • Published : 2021.09.30
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Abstract

Purpose: The purpose of this study is to propose a system predicting whether an electricity distribution system is abnormal by analyzing the temperature of the deteriorated system. Traditional electricity distribution system abnormality diagnosis was mainly limited to post-inspection. This research presents a remote monitoring system for detecting thermal images of the deteriorated electricity distribution system efficiently hereby providing safe and efficient abnormal diagnosis to electricians. Methods: In this study, an object detection algorithm (YOLOv5) is performed using 16,866 thermal images of electricity distribution systems provided by KEPCO(Korea Electric Power Corporation). Abnormality/Normality of the extracted system images from the algorithm are classified via the limit temperature. Each classification model, Random Forest, Support Vector Machine, XGBOOST is performed to explore 463,053 temperature datasets. The process capability index is employed to indicate the quality of the electricity distribution system. Results: This research performs case study with transformers representing the electricity distribution systems. The case study shows the following states: accuracy 100%, precision 100%, recall 100%, F1-score 100%. Also the case study shows the process capability index of the transformers with the following states: steady state 99.47%, caution state 0.16%, and risk state 0.37%. Conclusion: The sum of caution and risk state is 0.53%, which is higher than the actual failure rate. Also most transformer abnormalities can be detected through this monitoring system.

Keywords

Acknowledgement

이 논문은 2021년도 정부(과학기술정보통신부)의 재원으로 한국연구재단-현장맞춤형 이공계 인재양성 지원사업의 지원을 받아 수행된 연구임(No. 64420-27), 이 논문은 2021년도 정부(교육부)의 재원으로 한국연구재단4단계 두뇌한국(BK)21 사업 대학원 혁신지원을 받아 수행된 연구임.

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