Abstract
The demand for solar energy has rapidly increased throughout the world in recent years. However, anomalies in photovoltaic (PV) plants can reduce performances and result in serious consequences. Developing reliable statistical approaches able to detect anomalies in PV plants is vital to improving the management of these plants. Here, we present a statistical approach for detecting anomalies in the DC part of PV plants and partial shading. Firstly, we model the monitored PV plant. Then, we employ a generalized likelihood ratio test, which is a powerful anomaly detection tool, to check the residuals from the model and reveal anomalies in the supervised PV array. The proposed strategy is illustrated via actual measurements from a 9.54 PV plant.