Fault diagnosis with synchrosqueezing transform and optimized deep convolutional neural network: An application in modular multilevel converters

Abstract

High voltage direct current (HVDC) transmission mode with modular multilevel converters (MMC) topology is the future direction of transmission engineering, and security is their fundamental issue. Submodule fault of MMC in HVDC is the most common problem, nevertheless, traditional time–frequency based diagnosis technology can’t achieve high accuracy. To solve this pain spot, a new diagnosis strategy based on the synchrosqueezing transform (SST) and genetic algorithm optimized deep convolution neural network (GA-DCNN) is proposed in this paper. Firstly, the time–frequency representations (TFRs) of the raw signals which is synthesized by ac current and inner circulating current of the MMC are calculated with SST. Then, DCNN is introduced to learn the underlying features from the TFRs, and its key hyperparameters are optimized with genetic algorithm. Meanwhile, batch normalization, dropout and data augment technologies are explored to prevent DCNN model from overfitting and improve model performance. Compared to traditional SVM and BP-based algorithms, SST-GA-DCNN achieve high diagnosis accuracy. The experimental results show the feasibility and applicability of the proposed fault diagnosis framework.