Fault Diagnosis of SF6-Insulated Equipment by Micro Gas Sensor Array

Abstract

SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> decomposition products could reflect the running status and inner faults of power equipment, and it's expected to realize a timely warning. In this work, six faults including spark and corona discharge were simulated, and SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> decomposition products with various types and contents were obtained as well. Different from previous investigations employing precision instruments, such as gas chromatography and infrared spectroscopy, a micro sensor array loaded with three gas-sensitive nanomaterials was used to discriminate fault characteristic gases, performing obvious advantages in small size, high integration, and rapid detection. Gas chromatography-mass spectrometry (GCMS) indicated that seven analytes had significant differences in types and contents. Meanwhile, the as-prepared micro gas sensor array also outputted significantly various signals for seven analytes, which provided a basis for gas identification. With the assistance of stacked denoising autoencoder (SDAE)-based discrimination algorithms, the recognition model between the response signals of the array and the discharge faults in power equipment could be established. In comparison with KNN (66.67 %), decision tree (70.47 %), and BPNN (73.33 %), SVM has achieved the highest average accuracy of 75.23 %. Totally, this work provides a promising novel method for rapid on-site inspection of SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> -insulated power equipment.