Fault Diagnosis Method for UHVDC Transmission Based on Deep Learning under Cloud-Edge Architecture

Abstract

Aiming at the problem of fault diagnosis after the UHVDC system fails, a deep learning-based UHVDC fault diagnosis method under the cloud-edge architecture is proposed. First, based on the edge computing framework of the “cloud” + “edge terminal,” a four-layer fault diagnosis structure including the data integration layer, edge prediction layer, cloud diagnosis layer, and human-computer interaction layer is constructed. Then, a fault data set is constructed by finding effective information that can fully reflect the DC fault in the huge power grid environmental information, and the data set is screened, processed by classification feature fields, and linearly normalized. Finally, a deep convolutional generative adversarial network (DCGAN) is constructed by introducing a deep convolutional neural network (DCNN) into the traditional generative adversarial network (GAN) for data training and DC fault diagnosis. In addition, the corresponding process is given. The proposed method and the other three methods are compared and analyzed by simulation experiments. The results show that the method proposed has the highest accuracy and smallest error loss value of 95.6% and 0.18, respectively. It has the highest diagnosis accuracy under different fault types, and its performance is better than the other three comparison methods.