Fault Diagnosis for AC/DC Transmission System Based on Convolutional Neural Network

With the continuous expansion of the scale of AC/DC transmission systems, the power grid structure and fault characteristics become more and more complex. The existing fault diagnosis methods confront difficulty in accurately extracting fault characteristics in the face of complex power grid and large amount of data. There is an urgent need for power grid fault diagnosis methods with strong adaptability and high accuracy. Therefore, a convolutional neural network (CNN) based power grid fault diagnosis method is proposed. Firstly, it is tested step by step through the network construction mode of layer-by-layer screening and layer-by-layer superposition to build a network structure fully suitable for power grid fault diagnosis. Then, the network level optimization strategy is used to adjust the training parameters, and the deep fault features are mined with the goal of minimizing the cross entropy. Finally, the AC/DC transmission system model is built on MATLAB/Simulink platform, combined with t-distributed stochastic neighbor embedding (t-SNE) interpretability technology to show the diagnosis effect. Compared with traditional methods, it is proven that the proposed method can deeply mine fault characteristics and has high diagnosis accuracy. © 2022 Automation of Electric Power Systems Press.