Abstract: To address low identification sensitivity of the current power equipment fault identification system,a power equipment fault identification model based on cloud computing correlation analysis is proposed. The fault features of the power equipment are extracted and classified by using the correlation analysis method,the Model-1 fault feature extraction method and the copula function fault feature classification method. The classified feature data are randomly composed of training set X,and on this basis,the two-dimensional data of fault feature optimization is obtained. The output result of Copula function is introduced into the lost circulation type classification algorithm of optimized ID3 to optimize the fault characteristics,and the fault characteristic classification matrix of the power equipment is obtained. The CNN model of the asymmetric convolution layer is used to realize the rapid identification of multiple fault types of power equipment. The experimental results show that the average fault recognition rate of the proposed method is as high as 87.2% and the average recognition accuracy is as high as71.06%;In the test of the influence of different loads on the sensitivity of the system,the fault identification data count of the proposed method under any load state is not less than 40times,which is better than the compared method;In the identification performance test of the turn to turn short circuit fault location of the power equipment,the fault identification data count of the proposed method at any turn to turn short circuit fault location is higher than 140 times,which is better than the compared method. The proposed method has high accuracy and high sensitivity of fault location identification,which can promote the development of power grid safe operation.