基于半导体气体传感阵列的电缆过热故障诊断方法

doi:10.19595/j.cnki.1000-6753.tces.220585

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摘要

中低压电缆过载运行严重、工作环境恶劣、过热故障频发,往往是电气火灾的高发源头所在,但目前仍缺乏经济可靠的过热故障诊断方法。火灾早期阶段气体挥发物一般先于烟雾颗粒出现,因此该文开展基于气体传感阵列的电缆过热故障诊断研究。首先,以中低压电缆常用的绝缘材料聚氯乙烯（PVC）为研究对象,探索电缆绝缘材料过热生成气体组分,指导用于检测电缆过热分解气体的半导体气体传感器选型。然后,基于分解气体类型,构建气体传感器阵列,采用真实电缆模拟过热故障,测试气体传感器阵列对电缆过热分解气体的响应。最后,对传感器阵列的响应信号提取4 维时域特征、6 维频域特征和10 维曲线拟合特征,根据卡方检验选择前10个最高分数的特征组成特征矩阵,采用支持向量机进行分类,可以将过热程度分为正常、预警、报警三类温度区间,模型的准确率可达95%。整个实验过程中,烟雾报警器都没有发出报警蜂鸣。

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关键词 ：电缆过热, 分解气体, 气体传感器, 阵列模式识别

Abstract：

Low- and medium-voltage cables with severe overload operation, harsh working environment and frequent overheating faults are often the source of high incidence of electrical fires, but there is a lack of economical and reliable overheating fault diagnosis methods in the early stages of fire. Gas component analysis has been applied to transformers (dissolved gas in oil detection method) and the diagnosis of overheating and discharge faults in SF6 and its alternative gas insulated equipment. In the early stage of fire, gas volatiles generally appear before smoke particles, and gas component analysis can provide faster early warning of fire than the traditional smoke detection method, so this paper carried out research on the cable overheating fault diagnosis method based on gas sensing array.
Firstly, GCMS was used to analyze the gas components of cable overheating decomposition. The gas decomposition components at different temperatures was compared, the number of gas components and the change of main component had been discussed. The result of GCMS showed that the decomposition products of superheated PVC contains aromatic compounds, esters, aldehydes, ketones, saturated hydrocarbons, unsaturated hydrocarbons and other volatile organic compounds VOCs. With the increase of superheat temperature, the types of cable decomposition gas increased significantly. Among the decomposition gas, benzene, 2-EH, DOTP and DEHA were the four components with the highest gas concentrations. 2-EH exists in the whole temperature range of 90~200℃, DOTP and DEHA exist only at 140℃ and above, and the concentration of these three substances varies widely. Benzene mainly exists in the high temperature and serious overheating state above 180℃, and its content increases sharply at 200℃.
Based on the above analysis results, a variety of commercial sensors for VOCs detectionwere selected to compose the gas sensor array. A cable overheating fault simulation experiment was conducted to record the response curves of the sensor array, and the cable was heated to different temperatures. As the cable superheat temperature increases, the gas sensor array response curve showed a trend of faster response rate and increased response amplitude. Most gas sensors reaching saturation in response within 5 min after the heating temperature stabilized.As a comparison, in the temperature range of 70~300℃, the smoke alarm never alarmed.The collected response data weretaken within 5 min after temperature stabilization, and 20 features wereextracted, including time domain features, frequency domain features, and curve fitting features.The effects of 4 feature dimensionality reduction methods such as principal component analysis,Select K Best, tree estimation, SelectFromModel, etc. werecompared and Select K Best worked best. The optimal feature dimensionality reduction method was selected and the features were redeuced from 20 dimensions to 10 dimensions. After this, the mapping relationship between cable overheating temperature and response features was established using machine learning algorithms. The classification accuracy of 8 machine learning algorithms such as Extra Tree, Random Forest, Decision Tree, K Near Neighbor, Logistic Regression, Naive Bayes, Adaptive Boosting, and AdaBoost were compared. SVM works best in the classifier on the test set, which can classify the cable operating temperature into 3 different states such as normal (temperature up to 90℃), warning (temperature 120℃~160℃) and alarm (temperature above 180℃), and the accuracy of the model reaches 95%.
The conclusions of the above studies are as follows: (1) Cable superheat decomposition gas contains aromatic compounds, esters, aldehydes, ketones, saturated hydrocarbons, unsaturated hydrocarbons and other volatile organic compounds VOCs, so this paper selected a variety of commercial sensors for VOCs detection to form a sensor array. (2) As the cable superheat temperature increases, the response speed of the gas sensor response curve becomes faster and the response amplitude increases, and most gas sensors reach saturation in response within 5 min after the heating temperature stabilizes. In the temperature range of 70~300℃, the smoke detectordidn't alarm. (3) By extracting 20 features from the sensor array response signal within 5 minutes and filtering the first 10 optimal features according to the chi-square test and using SVM classification, the cable operating temperature can be classified into three states, such as normal (temperature up to 90℃), warning (temperature 120℃~160℃) and alarm (temperature higher than 180℃), and the accuracy of the model on test set reaches 95%. The cable overheating fault diagnosis method established in this paper has significant advantages over smoke alarms in terms of alarm timeliness as well as temperature interval identification accuracy.

Key words： Cable overheating decomposition of gases gas sensor arrays pattern recognition

收稿日期: 2022-04-12

PACS:

TP212

通讯作者: 杨爱军男,1986 年生,教授,博士生导师,研究方向人工智能技术、微纳传感器技术和电力设备故障诊断与寿命评估。E-mail：yangaijun@xjtu.edu.cn

作者简介: 雷芳菲女,1997 年生,博士研究生,研究方向为气体传感器在电缆故障诊断中的应用。E-mail：lff123456@stu.xjtu.edu.cn

引用本文:

雷芳菲, 褚继峰, 刘洋, 杨爱军, 袁欢, 王小华, 荣命哲. 基于半导体气体传感阵列的电缆过热故障诊断方法[J]. 电工技术学报, 0, (): 230639-230639. Lei Fangfei, Chu Jifeng, Liu Yang, Yang Aijun, Yuan Huan, Wang Xiaohua, Rong Mingzhe. Fault Diagnosis of Cable Overheating Based on Semiconductor Gas Sensing Array. Transactions of China Electrotechnical Society, 0, (): 230639-230639.

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