Online fault diagnosis of photovoltaic modules based on multi-class support vector machine

Efficient condition monitoring and fault diagnosis is an essential task to ensure the generation performance and reliability of photovoltaic (PV) systems. This paper proposes an online algorithm to diagnose faults of PV module based on multi-class support vector machine (M-SVM). The simulation models of the photovoltaic module are implemented and the output power generation characteristics of PV modules under two typical fault conditions (line-to-line fault and abnormal degradation fault) are analyzed. Through the combination of mathematical model and simulation experiments of PV modules, the fill factor (FF) and the fault type factor K are introduced as feature parameters in the fault diagnosis process. In addition, to address the nonlinear problem in fault diagnosis and improve the single support vector machine, a fault diagnosis method based on the multi-class classification method of one-against-one (OAO) algorithm is proposed. The proposed solution is implemented and simulated through experiments and the numerical result clearly demonstrates its effectiveness and diagnosis accuracy.