Abstract：The modular multilevel converter (MMC) is a new type of multilevel converter that features low switch frequency, high output waveform quality, and low consistency requirements for power electronic device switching. Therefore, it has been widely applied in various fields such as flexible DC transmission, power electronic transmission, and rail transit traction control. However, the increase in the number of sub-modules also means an increase in potential failure points. To meet the growing demands for system safety and reliability, timely and effective diagnosis needs to be carried out after system failures. Existing MMC fault diagnosis methods mainly fall into two categories: additional hardware circuit-based and software algorithm-based. Among them, the hardware-based method exhibits excellent diagnostic accuracy and short diagnostic time but may incur additional hardware costs and wiring complexity, while the software-based method does not require additional hardware circuitry and is relatively simple and convenient to implement. However, existing software-based method often heavily depends on the capacitance voltage information of sub-modules during the diagnosis process. The excessive number of voltage sensors not only increases the complexity of system wiring but also adds to the potential failure points of the system. To this end, a deep learning-based IGBT open-circuit fault diagnosis method for MMC sub-modules using a minimum number of voltage sensors is proposed, which only requires two arm voltage sensors for each phase. Based on the analysis of open-circuit fault characteristics of MMC sub-module IGBTs, the bridge arm voltage is selected as the required electrical parameter for fault diagnosis. The short-time Fourier transform (STFT) is utilized to extract harmonic component information from the bridge arm voltage signal. By arranging frequency domain information at different sampling times, the matrix containing time-frequency domain information is constructed as the diagnostic sample. By combining with the deep belief network (DBN), an IGBT open-circuit fault detection method based on STFT-DBN is proposed, which realizes the rapid diagnosis of the fault type. Meanwhile, to address the problem of relatively low location accuracy of the DBN, a convolutional neural network (CNN) based faulty sub-module location network is established for each fault type after the fault type is identified by the fault detection network. Combining these two methods allows for rapid fault type detection and high-accuracy faulty sub-module location. A 129-level MMC simulation model was established using Matlab/Simulink, and arm voltage information was collected under normal operation and various fault conditions. By applying the proposed method for data preprocessing, input samples were constructed, and the dataset was divided. Then the fault diagnosis networks were trained and tested separately, and the effectiveness of the proposed method was verified based on simulation data. Finally, the hardware and software of an MMC experimental system were designed in the laboratory environment, and an experimental system with reduced power levels was constructed. The control unit was implemented by a combination of DSP TMS320F28335 and FPGA, and various fault conditions were simulated by changing the drive signals. Arm voltage sensors were used to collect data from the experimental system under different fault conditions. The experimental results show that the proposed method can achieve high-precision and short-time fault diagnosis by using only arm voltage information.
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