Diagnosis of Partial Discharge Based on the Air Components for the 10 kV Air-Insulated Switchgear

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 6; p. 2395
• Main Authors: Tan, Qipeng, Zhang, Tiandong, Wu, Shaocheng, Gao, Jiachen, Song, Bin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 20.03.2022; MDPI
• Subjects: air-insulated switchgear; Algorithms; Breakdowns; Communication; Decomposition; Defects; Electric fields; Electricity distribution; Electrodes; Experiments; fault classification; gas detection; Gases; Machine learning; Neural networks; partial discharge (PD); Power; random forests; Sensors; Trends; air-insulated switchgear; random forests; partial discharge (PD); fault classification; gas detection
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22062395

Partial discharge (PD) is a common phenomenon of insulation aging in air-insulated switchgear and will change the gas composition in the equipment. However, it is still a challenge to diagnose and identify the defect types of PD. This paper conducts enclosed experiments based on gas sensors to obtain the concentration data of the characteristic gases CO, NO2, and O3 under four typical defects. The random forest algorithm with grid search optimization is used for fault identification to explore a method of identifying defect types through gas concentration. The results show that the gases concentration variations do have statistical characteristics, and the RF algorithm can achieve high accuracy in prediction. The combination of a sensor and a machine learning algorithm provides the gas component analysis method a way to diagnose PD in an air-insulated switchgear.