LAUSR

Photovoltaic (PV) systems have gained global acceptance in terms of green, replenishable energy resources to meet energy demand with no emissions. However, PV systems are susceptible to operational and environmental stresses. Moreover, PV panels monitoring is necessary to keep their performance and efficiency intact due to their lack of supervisory control. Therefore, this study monitors PV panels based on health into three sub-classes: healthy, hotspot, and faulty through infrared thermography. First, Thermographs key points are selected using an $8\times 8$ uniform pixel grid, and speed-up robust features (SURF) are extracted from grid intersection points. Afterward, due to its simplicity, the k-mean clustering algorithm creates single-level clusters based on actual observations similarities and similar observations closeness within-cluster and dissimilarity to other clusters observations are used to transform features into visual words. Finally, shallow classifiers are utilized because of low training time and high prediction speed. After extensive testing and compressive analysis, the proposed approach was found economical, fast, and showed high testing accuracy of 97% through a multi-class shallow classifier (support vector machine) with low computational complexity and less storage size. Thus, this approach can monitor megawatt PV systems with high accuracy and keep performance and emissions mitigation potential high while lowering payback time.