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%).
王琼苑, 褚继峰, 李秋霖, 杨爱军, 袁欢, 荣命哲, 王小华. 基于微型气体传感阵列的空气绝缘设备放电故障识别[J]. 电工技术学报, 2023, 38(23): 6494-6502.
Wang Qiongyuan, Chu Jifeng, Li Qiulin, Yang Aijun, Yuan Huan, Rong Mingzhe, Wang Xiaohua. Miniature Gas-Sensing Array Employed for the Discharge Fault Diagnosis of Air-Insulated Equipment. Transactions of China Electrotechnical Society, 2023, 38(23): 6494-6502.
[1] Chu Jifeng, Li Weijuan, Yang Xu, et al.Identification of gas mixtures via sensor array combining with neural networks[J]. Sensors and Actuators B: Chemical, 2021, 329: 129090. [2] 林涛, 韩冬, 钟海峰, 等. 工频交流电晕放电下SF6气体分解物形成的影响因素[J]. 电工技术学报, 2014, 29(2): 219-225. Lin Tao, Han Dong, Zhong Haifeng, et al.Influence factors of formation of decomposition by-products of SF6 in 50Hz AC corona discharge[J]. Transactions of China Electrotechnical Society, 2014, 29(2): 219-225. [3] 李进, 赵仁勇, 杜伯学, 等. 电工环氧绝缘件缺陷无损检测方法研究进展[J]. 电工技术学报, 2021, 36(21): 4598-4607. Li Jin, Zhao Renyong, Du Boxue, et al.Research progress of nondestructive detection methods for defects of electrical epoxy insulators[J]. Transactions of China Electrotechnical Society, 2021, 36(21): 4598-4607. [4] Zhang Xiaoxing, Li Yi, Tian Shuangshuang, et al.Decomposition mechanism of the C5-PFK/CO2 gas mixture as an alternative gas for SF6[J]. Chemical Engineering Journal, 2018, 336: 38-46. [5] Zhang Xiaoxing, Gui Yingang, Zhang Ying, et al.Influence of humidity and voltage on characteristic decomposition components under needle-plate discharge model[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2016, 23(5): 2633-2640. [6] Chu Jifeng, Wang Qiongyuan, Liu Yuyang, et al.Fault diagnosis of SF6-insulated equipment by micro gas sensor array[J]. IEEE Transactions on Power Delivery, 2023, 38(1): 222-230. [7] Li Yi, Zhang Xiaoxing, Zhang Ji, et al.Assessment on the toxicity and application risk of C4F7N: a new SF6 alternative gas[J]. Journal of Hazardous Materials, 2019, 368: 653-660. [8] 李康, 郭润睿, Javed H, 等. 空气局部放电衍生物气体生成规律的研究[J]. 电工电能新技术, 2017, 36(8): 1-7. Li Kang, Guo Runrui, Javed H, et al.Study of air by-products formation characteristics under corona discharge[J]. Advanced Technology of Electrical Engineering and Energy, 2017, 36(8): 1-7. [9] 张振宇, 晋涛, 王天正, 等. 基于气体检测的空气开关柜绝缘缺陷识别[J]. 武汉大学学报(工学版), 2021, 54(8): 761-768. Zhang Zhenyu, Jin Tao, Wang Tianzheng, et al.Insulation defect recognition of air switchgear based on gas detection[J]. Engineering Journal of Wuhan University, 2021, 54(8): 761-768. [10] 张晓星, 周磊, 裘吟君, 等. 针-板缺陷开关柜局部放电与空气分解组分的关联特性[J]. 高电压技术, 2016, 42(12): 3948-3954. Zhang Xiaoxing, Zhou Lei, Qiu Yinjun, et al.Correlation character between switch gear′s partial discharge quantity and air decomposition components under needle-plate defect model[J]. High Voltage Engineering, 2016, 42(12): 3948-3954. [11] Tang Ju, Zeng Fuping, Zhang Xiaoxing, et al.Relationship between decomposition gas ratios and partial discharge energy in GIS, and the influence of residual water and oxygen[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2014, 21(3): 1226-1234. [12] Luo Zongchang, Han Fangyuan, Tang Bin, et al.Optical properties and decomposition mechanisms of SF6 at different partial discharge determined by infrared spectroscopy[J]. AIP Advances, 2018, 8(6): 065107. [13] Rai P, Raj S, Ko K J, et al.Synthesis of flower-like ZnO microstructures for gas sensor applications[J]. Sensors and Actuators B: Chemical, 2013, 178: 107-112. [14] Nam B, Ko T K, Hyun S K, et al.NO2 sensing properties of WO3-decorated In2O3 nanorods and In2O3-decorated WO3 nanorods[J]. Nano Convergence, 2019, 6(1): 40. [15] Kumar R, Goel N, Kumar M.UV-activated MoS2 based fast and reversible NO2 sensor at room temperature[J]. ACS Sensors, 2017, 2(11): 1744-1752. [16] Na C W, Kim J H, Kim H J, et al.Highly selective and sensitive detection of NO2 using rGO-In2O3 structure on flexible substrate at low temperature[J]. Sensors and Actuators B: Chemical, 2018, 255: 1671-1679. [17] Wang Jing, Wang Caileng, Liu Ao, et al.High-response mixed-potential type planar YSZ-based NO2 sensor coupled with CoTiO3 sensing electrode[J]. Sensors and Actuators B: Chemical, 2019, 287: 185-190. [18] 谭桂霞, 陈烨璞, 徐引娟, 等. 氧气中臭氧分解规律[J]. 上海大学学报(自然科学版), 2007, 13(1): 82-84. Tan Guixia, Chen Yepu, Xu Yinjuan, et al.Rules of ozone decomposition in oxygen[J]. Journal of Shanghai University (Natural Science Edition), 2007, 13(1): 82-84. [19] Perrin A, Toon G, Orphal J.Detection of atmospheric 15NO2 in the ν3 spectral region (6.3μm)[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 2015, 154: 91-97. [20] Chaisriratanakul W, Bunjongpru W, Pankiew A, et al.Modification of polyvinyl chloride ion-selective membrane for nitrate ISFET sensors[J]. Applied Surface Science, 2020, 512: 145664. [21] Chu Jifeng, Wang Qiongyuan, Yang Aijun, et al.Method of sieving the optimal NO2 sensitive material[J]. Sensors and Actuators B: Chemical, 2023, 375: 132929. [22] 张晓星, 姚尧, 唐炬, 等. SF6放电分解气体组分分析的现状和发展[J]. 高电压技术, 2008, 34(4): 664-669, 747. Zhang Xiaoxing, Yao Yao, Tang Ju, et al.Actuamity and perspective of!Proximate analysis of SF6 decomposed products under partial discharge[J]. High Voltage Engineering, 2008, 34(4): 664-669, 747. [23] Kumar R, Al-Dossary O, Kumar G, et al.Zinc oxide nanostructures for NO2 gas-sensor applications: a review[J]. Nano-Micro Letters, 2015, 7(2): 97-120. [24] 韩翔宇, 纽春萍, 何海龙, 等. 电磁式断路器状态监测与智能评估技术综述[J]. 电工技术学报, 2023, 38(8): 2191-2210. Han Xiangyu, Niu Chunping, He Hailong, et al.Review of condition monitoring and intelligent assessment of electromagnetic circuit breaker[J]. Transations of China Electrotechnical Society, 2023, 38(8): 2191-2210. [25] 顾崇寅, 徐潇源, 王梦圆, 等. 基于CatBoost算法的光伏阵列故障诊断方法[J]. 电力系统自动化, 2023, 47(2): 105-114. Gu Chongyin, Xu Xiaoyuan, Wang Mengyuan, et al.CatBoost algorithm based fault diagnosis method for photovoltaic arrays[J]. Automation of Electric Power Systems, 2023, 47(2): 105-114. [26] 彭莎, 孙铭阳, 张镇勇, 等. 机器学习在电力信息物理系统网络安全中的应用[J]. 电力系统自动化, 2022, 46(9): 200-215. Peng Sha, Sun Mingyang, Zhang Zhenyong, et al.Application of machine learning in cyber security of cyber-physical power system[J]. Automation of Electric Power Systems, 2022, 46(9): 200-215. [27] 姜雅男, 于永进, 李长云. 基于改进TOPSIS模型的绝缘纸机-热老化状态评估方法[J]. 电工技术学报, 2022, 37(6): 1572-1582. Jiang Yanan, Yu Yongjin, Li Changyun.Evaluation method of insulation paper deterioration status with mechanical-thermal synergy based on improved TOPSIS model[J]. Transactions of China Electrotechnical Society, 2022, 37(6): 1572-1582. [28] 崔芮华, 李泽, 佟德栓. 基于三维熵距和熵空间的航空电弧故障检测与分类技术[J]. 电工技术学报, 2021, 36(4): 869-880. Cui Ruihua, Li Ze, Tong Deshuan.Arc fault detection and classification based on three-dimensional entropy distance and entropy space in aviation power system[J]. Transactions of China Electrotechnical Society, 2021, 36(4): 869-880.
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