A Review of Power Equipment Defect Detection Based On Deep Learning

The defect of power equipment will lead to the failure of power system, which will cause serious economic loss and social harm. Power equipment defect detection is an important work to ensure the stable operation of power system. In recent years, power equipment defect detection based on deep learning has made remarkable progress. This paper discusses the selection and application of deep learning algorithm in the field of power equipment defect detection, reviews the existed research, and analyzes the research background and significance of power equipment defects. This paper introduces the application of CNN, RNN, GAN and DBN algorithm models in the field of abnormal temperature, pressure and current detection of power equipment. The problems and challenges of algorithm performance, interpretability and test criteria are discussed, and the development prospects in the field of power equipment defect detection are summarized and prospected.