Automatic Diagnosis System for Partial Discharge of Power Grid Cables Based on Convolutional Neural Network
Design an automated diagnosis system for partial discharge of power grid cables based on convolutional neural networks to address issues such as difficulty in matching specific discharge faults with signals in the current process of cable partial discharge diagnosis.Collect partial discharge signals from power grid cables,convert the sig-nals into two-dimensional images,use the two-dimensional images of discharge signals as training samples for convo-lutional neural networks,and train the convolutional neural network.Collect test samples and use a trained convolu-tional neural network to achieve automated diagnosis of partial discharge in power grid cables.The experimental re-sults show that under the condition of 100 convolutional kernels,the system can obtain satisfactory diagnostic results based on GAF time series transformation of image features,reduce the time of partial discharge faults in the research object,and improve the operational stability of the research object.
convolutional neural networkpower grid cablespartial dischargeautomated diagnosisGram angle field(GAF)pulse voltage signal
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