In this paper, a health monitoring method for photovoltaic (PV) systems based on probabilistic neural network (PNN) is proposed that detects and classifies short-and opencircuit faults in real time. To implement and validate the proposed method in computer programs, a new approach for modeling PV systems is proposed that only requires information from manufacturer's datasheet reported under normal-operating cell temperature (NOCT) conditions and standard-operating test conditions (STCs). The proposed model precisely represents characteristics of PV systems at different temperatures, as the temperature dependency of parameters such as ideality factor, series resistance, and thermal voltage is considered in the proposed model. Although this model can be applied to a variety of applications, it is specifically used to test and validate the performance of the proposed fault detection and classification method. Index Terms-Fault detection, monitoring systems, photovoltaic (PV) modeling, probabilistic neural network (PNN).

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