To fully utilize the time-frequency information contained in the IGBT open-circuit fault current signals of voltage source controlled static synchronous compensator (VSC-STATCOM) and improve the accuracy of fault diagnosis and identification, a novel WST-RCA-ICA-Res algorithm is proposed. This algorithm combines wavelet scattering transform (WST) with an improved residual channel attention (IRCA) module, and an improved coordinate attention (ICA) module with a residual neural network (Resnet). First, the Matlab/Simulink platform is used to simulate 22 types of faults in the VSC-STATCOM module under different operating conditions to obtain the fault sample set. Then, automatic robust feature extraction of fault signals is carried out using WST to construct a feature matrix containing time-frequency information. Finally, the IRCA-ICA-Res model is used to deeply extract, strengthen, and identify the feature matrix. Experimental results show that the proposed method has strong anti-noise performance and can accurately distinguish different IGBT fault types.