Cable Fault Anomaly Analysis and Feature Classification Algorithm Based on Neural Network Deep Learning
In order to solve the cable fault anomaly analysis,this paper studies the convolutional neural network model and feature classification algorithm.The types and causes of cable faults are analyzed in detail,and cable operation data from multiple power systems are collected for processing.In the construction and training of deep learning model and feature classification algorithm,the technical details and parameters are optimized to achieve the best classification performance.At the same time,the feature classification algorithm is designed and used to classify the data from time domain to frequency domain by SVM random forest.The experimental results show that the proposed method performs well in the analysis of cable fault anomalies,and has a high degree of superior automation performance and strong generalization ability.Through the research of this paper,an efficient and accurate method is provided for the analysis of cable fault anomalies.
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