Health Monitoring and Fault Detection in Photovoltaic Systems in Central Greece Using Artificial Neural Networks

Featured Application The health monitoring of a photovoltaic park is essential for a number of reasons, including early action to safeguard the profitability of investment and the timely support of claims regarding PV panels' performance degradation. This study was motivated by the previous, successful application of machine learning in the prediction of a photovoltaic plant's output based on monitoring data. An extension of these applications to the health monitoring and fault diagnosis of photovoltaic parks is proven successful, as demonstrated in this paper. The results are applicable to the systematic performance monitoring and fault detection of photovoltaic installations. The operation and maintenance of a photovoltaic system is a challenging task that requires scientific soundness, and has significant economic impact. Faults in photovoltaic systems are a common phenomenon that demands fast diagnosis and repair. The effective and accurate diagnosis and categorization of faults is based on information received from the photovoltaic plant monitoring and energy management system. This paper presents the application of machine learning techniques in the processing of monitoring datasets of grid connected systems in order to diagnose faults. In particular, monitoring data from four photovoltaic parks located in Central Greece are analyzed. The existing data are divided for training and validation procedures. Different scenarios are examined first, in order to observe and quantify the behavior of artificial neural networks in already known faults. In this process, the faults are divided in three main categories. The system's performance deviation against the prediction of the trained artificial neural network in each fault category is processed by health monitoring methodology in order to specify it quantitatively.