Since photovoltaic (PV) arrays installationin in harsh outdoor environments, PV faults frequently occur during their operation. To accurately identify the fault types of PV arrays, a sand cat swarm optimization support vector machine (SCSO-SVM) was proposed for PV module fault identification. In addition, the SCSO-SVM, support vector machine (SVM), particle swarm optimized support vector machine (PSO-SVM), genetic optimized support vector machine (GA-SVM), sparrow optimized support vector machine (SSA-SVM), gray wolf optimized support vector machine (GWO-SVM) and whale optimized support vector machine (WOA-SVM) algorithms were compared. First of all, all six SVM hybrid algorithms overcome the disadvantage that SVM diagnosis results were easily affected by the initial values of parameters, and the recognition accuracy was improved compared with traditional SVM algorithms, but the recognition time was increased for all of them. Secondly, SCSO-SVM recognition was the best among the seven algorithms, which overcomed the vulnerability of SVM to the initial values of parameters and improved the recognition accuracy by about 9.459 4% compared to SVM. Because it is more effective in finding the SVM penalty factors and kernel function parameters. Then, for the same algorithm, the recognition accuracy of the algorithm decreased with decreasing input features because the fewer the input features, the less effective it was to characterize the output properties of the PV modules under different fault types. However, the recognition time of the algorithm was not brief with the decrease of the input features. Therefore, the appropriate input features were selected to balance the fault recognition accuracy and efficiency of the algorithm. Finally, it was found that the recognition effect of the seven algorithms depends on the effect of the dataset. The reason may be that there are differences in generalizability due to excessive selection of parameters for each algorithm and dependence on the initial value selection of parameters.