Compound Fault Diagnosis Method of Modular Multilevel Converter Based on Improved Capsule Network

Abstract

When submodule (SM) compound fault occurs in the Modular multilevel converter (MMC), the time-domain waveform characteristics of output current and internal circuiting current of MMC are not obvious, especially on the condition of high-level MMC. To address this issue, A compound fault diagnosis method based on an improved capsule network (CapsNet) is proposed in this paper. Firstly, The front end of the network adopts the feature extraction structure of One-dimensional Convolutional Neural Network (1DCNN) combined with Long and short-term memory network (LSTM). Then, the original data of MMC three-phase output currents and three-phase circulating currents are used as fault detection signal. The back-end employs a main capsule layer and a digital capsule layer structure, and realizes the transfer of feature vectors through a dynamic routing algorithm. This feature extraction structure combines the light weight of 1DCNN and the sequential sensitivity of LSTM. While ensuring that the information is fully extracted, the computational cost of the model is greatly reduced. Furthermore, the overlapping sampling method is used to construct and expand the sample sets. Compared with the other three common deep learning methods, their fault diagnosis performance under the condition of different levels and levels changing are analyzed respectively. The experimental results demonstrate that the proposed method has excellent cross-domain learning ability and high fault recognition accuracy.