Deep learning technology is increasingly used in the field of power system fault detection and diagnosis, and its powerful feature learning capability makes it play an important role in intelligent process control. In this paper, we propose a method for high resistance fault detection in power systems and design a CNN-Attention-LSTM fault diagnosis model using various deep learning models such as convolutional neural network. The model training and simulation experiments are carried out on the collected power fault dataset. The accuracy, reliability and security of the proposed power fault detection method for high resistance fault phase identification are 99.5%, 99.8% and 99.2%, respectively. The model can accurately classify cable faults in cable fault diagnosis, and also has better diagnostic effect on transformer faults in the power system, in which the diagnostic accuracy of harmonic faults is as high as 100%, showing better fault classification and diagnosis performance.