Abstract:Partial discharge (PD) detection is the main means of power equipment status evaluation at present. Due to the complexity of defect map and the diversity of field interference, the traditional local discharge pattern recognition method has low recognition accuracy and long training time. Based on these problems, an intelligent recognition method of partial discharge map of power equipment based on image processing technology and sparse data depth de-noising is proposed in this paper. Firstly, image processing technology is used to preprocess the detected image data. Then the active de-noising is carried out by using the deep sparse de-noising self-encoder. Finally, extreme learning machine is used as classifier to realize intelligent classification and identification of partial discharge. Using this method to verify the measured data at the scene of the substation, prove that the method of partial discharge signals containing various interference has better recognition effect, can apply to the current electric power equipment images information pattern recognition applications.