The power generation modules in the bridge arm of the modular multilevel converter (MMC) half-bridge series microgrid system are connected in series, and its input and removal are realized by turning on and off the insulated gate bipolar transistor (IGBT) in the half-bridge converter (HC). However, under the grid-connected double closed-loop control of the system, if a fault occurs in the HC in the bridge arm and its connecting lines, it will have a certain impact on the output characteristics of the system. For this reason, the changes of the bridge arm output voltage and current, inter-phase circulating current, grid-connected current and other parameters are analyzed when the IGBT and its anti-parallel diode in the HC has an open-circuit or short-circuit fault, and the connection line between the HC's has an open-circuit fault. Parameters with obvious abnormal changes are selected as feature attributes and used to construct a sample data set. In addition, in the fault diagnosis of the system bridge arm, a fault diagnosis model based on a whale-optimized support vector machine (WO-SVM) is established to solve the problem of low accuracy when using traditional SVM. Combined with different data sets, the validity of the model is verified through simulation. The results show that compared with the traditional SVM and BP neural network algorithms, the fault bridge arm diagnosis method based on the improved SVM has higher accuracy.