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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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, GC-MS 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 GC-MS 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

Received: 12 April 2022

PACS:	TP212
	TM854

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	Lei Fangfei
	Chu Jifeng
	Liu Yang
	Yang Aijun
	Yuan Huan
	Wang Xiaohua
	Rong Mingzhe

Cite this article:

Lei Fangfei,Chu Jifeng,Liu Yang等. Fault Diagnosis of Cable Overheating Based on Semiconductor Gas Sensing Array[J]. Transactions of China Electrotechnical Society, 2023, 38(13): 3651-3664.

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