Fault Diagnosis for Hybrid AC/DC Power System Based on Convolutional Neural Network with Transfer Learning

Deep learning methods have exhibited remarkable perforamance for fault diagnosis in power grid transmission lines. However, specific power systems often demand dedicated classifiers. Alterations to the structure of grid can negatively impact the accuracy of the previously trained models. Retraining a new model will cost a lot of time and sacrifice more economic resources. In such cases, transfer learning is able to transfer the knowledge acquired from a previous task to a new target task. Consequently, this notably decreases the training costs. In this study, a convolutional neural network (CNN) with transfer learning is implemented for fault diagnosis in AC/DC hybrid power systems with various structures. Through a series of simulations, feasibility and efficiency of this method for fault diagnosis are proved. This method also shows strong robustness in fault detection with varying fault inception angle, fault resistance, fault location, and system frequency shuffling. Moreover, the improved CNN model in this paper exhibits powerful generalization ability on test data. The aforementioned advantages illustrate the potentiality of this method for fault diagnosis in real power grid.