基于微型气体传感阵列的空气绝缘设备放电故障识别

doi:10.19595/j.cnki.1000-6753.tces.L10079

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Abstract As a natural insulating gas, air is widely used in power equipment (switchgear, ring cabinets, etc.). It will produce characteristic decomposition products represented by NO₂ when the power equipment occurs internal faults. The content of composition reflects the severity of the discharge fault. Therefore, the detection of gas decomposition products is helpful to the stable operation of power equipment. The cross-sensitivity of the sensor materials towards NO₂ and CO have seriously restricted the accuracy of fault diagnosis. To address these issues, this paper develops a miniature gas-sensing array with high sensitivity and great reliability, which aimed for the discharge fault diagnosis of air-insulated equipment.
This paper has simulated 15 air discharge faults including corona discharge, spark discharge, and arc discharge at different voltage levels and durations. After that, Fourier infrared tests were performed on the 15 gas samples. However, there has no apparent absorption peak of CO and O₃ in the infrared absorption spectrum. The absorption peak in 1 650~1 550 cm^-1 band corresponds to the antisymmetric stretching vibration of O=N=O chemical bond. This indicates the presence of NO₂ in the decomposition products of air. Besides, discharge with different voltages and times shows significant differences in the content of NO₂.
To discriminate fault characteristic gases, this paper has integrated a micro sensor array loaded with four gas-sensitive nanomaterials (10%WO₃-10%SnCl₂-In₂O₃, 5%NiO-10%SnCl₂-In₂O₃, 10%TiO₂-10%SnCl₂-In₂O₃ and 5%SnO₂-10%SnCl₂-In₂O₃). Then the paper obtains the repeatable gas response recovery curves under different discharge faults, which lays the foundation for the construction of gas identification model. The results demonstrate that the higher voltage and the longer discharge time means the larger response value of sensor array. Accordingly, it is further proved that the higher air-discharge voltages and times generates more characteristic decomposition gas. In addition, t-SNE dimensionality reduction has been employed to explore the identification ability of miniature gas-sensing array. The identification results of different air discharge faults are 84.88%, 81.82%, 76.86%, and 81.32% by four machine learning algorithms (Extra Tree, Decision Tree, KNN, and RF) respectively. And the recognition ability of the micro gas sensing array for partial discharge and arc discharge is slightly higher than that of spark discharge. The essential reason can be ascribed to the saturation of NO₂ in the process of multiple spark discharges.
The following conclusions can be drawn from the simulation analysis: (1) The FTIR of air discharge products shows that the arc discharge (200~400 μL/L) produces more NO₂ than corona and spark discharge (10~50 μL/L). Besides, the NO₂ concentration increases with the increase of discharge voltage and discharge time (times). (2) Compared with corona and spark discharge, the response value of the micro sensor array towards arc discharge products (6 kV, 4 033%) is nearly 3 times higher than that of spark discharge (15 kV, 5 times, 1 142.8%). (3) After the feature reduction of 10-dimensional PCA, we compared four machine learning algorithms, and found that the Extra Tree algorithm obtained the best identification accuracy of air discharge fault (84.88%).

Key words： Air insulation discharge fault simulation NO₂ detection micro sensor array fault diagnosis

Received: 13 January 2023

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TM930

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	Wang Qiongyuan
	Chu Jifeng
	Li Qiulin
	Yang Aijun
	Yuan Huan
	Rong Mingzhe
	Wang Xiaohua

Cite this article:

Wang Qiongyuan,Chu Jifeng,Li Qiulin等. Miniature Gas-Sensing Array Employed for the Discharge Fault Diagnosis of Air-Insulated Equipment[J]. Transactions of China Electrotechnical Society, 2023, 38(23): 6494-6502.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.L10079 OR https://dgjsxb.ces-transaction.com/EN/Y2023/V38/I23/6494

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