基于SCSO-SVM算法的光伏组件故障识别

光伏阵列通常被安装在恶劣的室外环境中,因此在运行过程中易发生故障。为了准确识别光伏阵列的故障类型,提出沙猫群优化支持向量机(sand cat swarm optimization support vector machine, SCSO-SVM)用于光伏组件故障识别,且对比支持向量机(support vector machine, SVM)、粒子群优化支持向量机(particle swarm optimized support vector machine, PSO-SVM)、遗传优化支持向量机(genetic optimized support vector machine, GA-SVM)、麻雀优化支持向量机(sparrow optimized support vector machine, SSA-SVM)、灰狼优化支持向量机(gray wolf optimized support vector machine, GWO-SVM)和鲸鱼优化支持向量机(whale optimized support vector machine, WOA-SVM)算法。首先,六种SVM混合算法都克...

Fault Identification of PV Modules Based on SCSO-SVM Algorithm

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) is proposed for PV module fault identification in this paper. 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 are compared. First of all, all six SVM hybrid algorithms overcome the disadvantage that SVM diagnosis results are easily affected by the initial values of parameters, and the recognition accuracy is im-proved compared with traditional SVM algorithms, but the recognition time is increased for all of them. Secondly, SCSO-SVM recognition is the best among the seven algorithms, which overcomes the vulnerability of SVM to the initial values of parameters and improves the recognition accuracy by about 9.4594% 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 decreases with decreasing input features because the fewer the input features, the less effective it is to　characterize the output properties of the PV modules under different fault types. However, the recognition time of the algorithm is not brief with the decrease of the input features. Therefore, the appropriate input features are 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.