Niazi, K.
(SBA School of Science and Engineering, Lahore University of Management Sciences (LUMS), Lahore, Punjab, 54792, Pakistan)
,
Akhtar, W.
(University of Heriot Watt, Edinburgh, UK)
,
Khan, H. A.
(SBA School of Science and Engineering, Lahore University of Management Sciences (LUMS), Lahore, Punjab, 54792, Pakistan)
,
Sohaib, S.
(University of Engineering and Technology, Taxila, Pakistan)
,
Nasir, A. K.
(SBA School of Science and Engineering, Lahore University of Management Sciences (LUMS), Lahore, Punjab, 54792, Pakistan)
Photovoltaic (PV) modules are subject to various internal or external stresses due to their operation in solar PV based power systems. Therefore, monitoring and maintenance are critical issues to ensure reliability of PV modules which in turn would affect the reliability of any PV system. In this pa...
Photovoltaic (PV) modules are subject to various internal or external stresses due to their operation in solar PV based power systems. Therefore, monitoring and maintenance are critical issues to ensure reliability of PV modules which in turn would affect the reliability of any PV system. In this paper, we categorize operational solar panels into two categories (Defective and Non-Defective panels) using a machine learning technique i.e. texture features through thermography assessment. Further, the panels are also categorized for diagnostic perspective using nBayes classifier. Results from an investigation for a 42.24 kWp PV system showed a mean recognition rate of 98.4% for a set of 260 test samples.
Photovoltaic (PV) modules are subject to various internal or external stresses due to their operation in solar PV based power systems. Therefore, monitoring and maintenance are critical issues to ensure reliability of PV modules which in turn would affect the reliability of any PV system. In this paper, we categorize operational solar panels into two categories (Defective and Non-Defective panels) using a machine learning technique i.e. texture features through thermography assessment. Further, the panels are also categorized for diagnostic perspective using nBayes classifier. Results from an investigation for a 42.24 kWp PV system showed a mean recognition rate of 98.4% for a set of 260 test samples.
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