Research on deep-learning-based fault diagnosis and prediction methods for electrical systems

With the breakthrough progress of deep learning technology in various fields, its application in fault diagnosis and prediction of electrical systems has received more and more attention. In this paper, a deep learning-based fault diagnosis and prediction model is proposed for the complex nonlinear characteristics in electrical systems. First, key features are automatically extracted from a large amount of electrical system operation data using a multilayer autoencoder (MLAE). These features are fed into a deep neural network (DNN) for fault classification and prediction. In order to improve the robustness and accuracy of the model, an attention mechanism is introduced so that the model pays more attention to the key features related to faults during the learning process. The method demonstrates high accuracy and reliability on multiple electrical system fault datasets compared to traditional electrical system fault diagnosis methods. In addition, this study explores the interpretability.