Contact Failure Diagnosis for GIS Plug-In Connector by Magnetic Field Measurements and Deep Neural Network Classifiers

This article presents a contact fault diagnosis method of gas-insulated switchgear (GIS) plug-in connector via magnetic field measurement, magnetic field visualization, and deep neural network (DNN) classifiers. First, the surrounding magnetic field of GIS plug-in connector with normal contact (NC) condition and with artificially designed contact failures was measured by the Hall sensor array. Then, the measured magnetic field was gathered with an original matrix of $16\times16$ dimensions. The $\vert $ original matrix was then visualized by the max–min normalization and correlation matrix. Database containing 11 000 magnetic field images was labeled and segmented as training, validation, and test datasets. Furthermore, high-dimensional features of input magnetic field images were extracted by different DNN filters, including convolutional neural network (CNN), simple recurrent neural network (Sim-RNN), and long short-term memory (LSTM) network. Then, extracted high-dimensional features were fed into a fully connected (Fc) neural network with SoftMax classifiers to identify different contact faults. Finally, the performance of different DNN-based classifiers is compared by the fault classification merits, $t$ -distributed stochastic neighbor embedding ( $t$ -SNE) feature clustering, and confusion matrixes. Results show that the DNN-based model could achieve contact fault classification task with an accuracy of 97.7% and $F_{1\_{}{\mathrm {score}}}$ of 0.985. Therefore, the proposed method is useful for designing a high-performance contact status monitoring system of GIS equipment, thus improving its operation safety.