Intelligent diagnosis and recognition method of GIS partial discharge data map based on deep learning

Abstract

Due to the large amount of data information in the Gas insulated switchgear (GIS) partial discharge defect sample database, if the analysis and diagnosis are directly carried out, there will be problems of slow data mining speed, poor efficiency and low accuracy. Therefore, further research is needed to apply to different types of insulation partial discharge data abnormalities of GIS equipment. The typical feature mining and fault diagnosis method of the state. First, Use the Association analysis rules and the neural network to make a data cleaning strategy to clean and normalize the sample data. Then use the self-learning algorithm evolution mechanism to train the deep neural network, update the model parameters, and continuously modify the abnormal state data of the sample library, and build the GIS equipment insulation defect partial discharge data sample knowledge base. Finally, a deep belief network partial discharge signal diagnosis neural network learning model and optimization technology based on deep learning are constructed to improve the adaptability and generalization of different types of partial discharge signal map diagnosis of GIS equipment, as well as the intelligent diagnosis speed and abnormal state information accuracy.