• Energy Research

Monitoring and maintenance of photovoltaic (PV) modules are critical for a reliable and efficient operation. Hotspots in PV modules due to various defects and operational conditions may challenge the reliability, and in turn, the entire system. From the monitoring standpoint, hotspots should be detected and categorized for subsequent maintenance. In this paper, hotspots are detected, evaluated, and categorized uniquely by using a machine learning technique on thermal images of PV modules. To achieve so, the texture and histogram of gradient (HOG) features of thermal images of PV modules are used for classification. The categorized hotspots are detected by training the machine learning algorithm, i.e., a Naive Bayes (nBayes) classifier. Experimental results are performed on a 42.24-kWp PV system, which demonstrates that a mean recognition rate of around 94.1% is achieved for the set of 375 samples.

Monitoring and maintenance of photovoltaic (PV) modules are critical for a reliable and efficient operation. Hotspots in PV modules due to various defects and operational conditions may challenge the reliability, and in turn, the entire system. From the monitoring standpoint, hotspots should be detected and categorized for subsequent maintenance. In this paper, hotspots are detected, evaluated, and categorized uniquely by using a machine learning technique on thermal images of PV modules. To achieve so, the texture and histogram of gradient (HOG) features of thermal images of PV modules are used for classification. The categorized hotspots are detected by training the machine learning algorithm, i.e., a Naive Bayes (nBayes) classifier. Experimental results are performed on a 42.24-kWp PV system, which demonstrates that a mean recognition rate of around 94.1% is achieved for the set of 375 samples.