Fault Diagnosis of Submodule Open-Circuit Failures for Modular Multilevel Converters With the Data Processing of Sensors

Driving increased consumption of clean energy, the low carbon energy system will be a significant technology to achieve the goal of carbon neutrality. Therefore, the safety and reliability of power equipment in low carbon energy systems is particularly important. The modular multilevel converter (MMC) is widely used in the field of flexible dc transmission and exhibits many advantages. However, in such systems, the enormous number of submodules (SMs) represents potential failure points. To overcome the problem of SM open-circuit for MMCs, a fault diagnosis with data processing of sensors is proposed in this article. The distortion of bridge arm current is used for fault detection, and the prediction of the SM capacitance voltage by whale optimization algorithm-least squares support vector machine (WOA-LSSVM) is used for fault location. All data of characteristic bridge arm current and the SMs capacitance voltage are extracted during normal operation and fault operation. To overcome the defect of parameter randomization of LSSVM neural network, the WOA algorithm is used to optimize the model and further improve its prediction accuracy. In addition, this technique does not require the additional sensors and complex mathematical models. A three-phase MMC prototype with four SMs per arm is built in the laboratory, and the simulation and experimental result confirm the effectiveness of the proposed method.