Article: State diagnosis technology of metal enclosed gas insulation equipment based on Apriori algorithm in cloud computing environment Journal: International Journal of Environment and Pollution (IJEP) 2026 Vol.76 No.5 pp.77 - 99 Abstract: In recent years, with the increasing failure rate of gas-insulated switchgear (GIS), there has been a growing need for people to understand the most common insulator issues. Therefore, real-time monitoring of its operating status is crucial to ensure the safe and reliable operation of power lines. This study proposes the use of Apriori algorithm (CFSA-AA) for cloud based fault state analysis to predict discharge faults, mechanical faults, and abnormal mechanical vibrations in metal enclosed GIS. This data is sourced from the Kaggle repository used for VSB power line fault detection. This study summarises GIS abnormal heating faults, including circuit breakers, isolating switches, shell grounding, and disc insulator bolts. The experimental results show that compared with other existing models, the proposed CFSA-AA model improves the accuracy of fault diagnosis to 98.9%, pattern discovery rate to 97.4%, operating state detection rate to 95.6%, Mattew correlation coefficient ratio to 96.4%, and error rate to 7.4%. Inderscience Publishers - linking academia, business and industry through research

Title: State diagnosis technology of metal enclosed gas insulation equipment based on Apriori algorithm in cloud computing environment

Authors: Jiayi Wang; Yuan Fang; Shaoqing Chen; Zongxi Zhang; Dianbo Zhou; Yuhang He; Jing Zhang

Addresses: Electric Power Research Institute, State Grid Sichuan Electric Power Company, Chengdu, 610048, Sichuan, China; State Grid Southwest Electric Power Research Institute Co. Ltd., Chengdu 610072, China ' State Grid Sichuan Electric Power Company, Chengdu, 610042, Sichuan, China ' Electric Power Research Institute, State Grid Sichuan Electric Power Company, Chengdu, 610048, Sichuan, China ' Electric Power Research Institute, State Grid Sichuan Electric Power Company, Chengdu, 610048, Sichuan, China ' Electric Power Research Institute, State Grid Sichuan Electric Power Company, Chengdu, 610048, Sichuan, China ' Electric Power Research Institute, State Grid Sichuan Electric Power Company, Chengdu, 610048, Sichuan, China ' Electric Power Research Institute, State Grid Sichuan Electric Power Company, Chengdu, 610048, Sichuan, China

Abstract: In recent years, with the increasing failure rate of gas-insulated switchgear (GIS), there has been a growing need for people to understand the most common insulator issues. Therefore, real-time monitoring of its operating status is crucial to ensure the safe and reliable operation of power lines. This study proposes the use of Apriori algorithm (CFSA-AA) for cloud based fault state analysis to predict discharge faults, mechanical faults, and abnormal mechanical vibrations in metal enclosed GIS. This data is sourced from the Kaggle repository used for VSB power line fault detection. This study summarises GIS abnormal heating faults, including circuit breakers, isolating switches, shell grounding, and disc insulator bolts. The experimental results show that compared with other existing models, the proposed CFSA-AA model improves the accuracy of fault diagnosis to 98.9%, pattern discovery rate to 97.4%, operating state detection rate to 95.6%, Mattew correlation coefficient ratio to 96.4%, and error rate to 7.4%.

Keywords: state diagnosis; metal enclosed gas insulation equipment; Apriori algorithm; cloud computing; fault detection; transmission lines.

DOI: 10.1504/IJEP.2026.152506

International Journal of Environment and Pollution, 2026 Vol.76 No.5, pp.77 - 99

Received: 22 Jul 2025
Accepted: 03 Dec 2025
Published online: 24 Mar 2026 *

Title: State diagnosis technology of metal enclosed gas insulation equipment based on Apriori algorithm in cloud computing environment

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