Research on intelligent diagnosis and recognition technology of GIS partial discharge data atlas based on deep learning

GIS plays an important role in power system,so when there is a failure in GIS,it will affect the stable operation of the whole power system, and even threaten the security issues,partial discharge is a common fault in GIS, and it can be caused by many reasons.The insulation fault can be predicted in advance by live detection or on-line monitoring of partial discharge. However, due to the low efficiency of the current equipment condition based maintenance, there is a lack of effective intelligent diagnosis algorithm. In view of this situation, an intelligent diagnosis and recognition technology of GIS partial discharge data atlas based on deep learning is proposed. Firstly, on the basis of deep learning neural network and data fusion theory, combined with the application of AI Artificial Intelligence Algorithm in UHF and ultrasonic partial discharge defect diagnosis of GIS equipment, the intelligent diagnosis model of UHF and ultrasonic partial discharge data Atlas of GIS equipment based on deep learning is constructed, and then the intelligent diagnosis and analysis system of GIS partial discharge data based on artificial intelligence is developed, Finally, the intelligent diagnosis and recognition of partial discharge data Atlas of GIS equipment are realized. The experimental results show that the data intelligent analysis system has excellent data classification and judgment performance in the face of multimodal data. In general, deep learning greatly improves its computational efficiency by using neural network pre training algorithm. In addition, its high efficiency in data fitting and classification makes it show great industrial application potential and commercial application value in the social production and life with the rapid development of information technology and the continuous expansion of data scale and complexity.