{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:52:49Z","timestamp":1758271969710,"version":"3.38.0"},"reference-count":32,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2024,2,20]]},"abstract":"<jats:p>In order to identify and locate flaws in solar thermal images, this research suggests using an optimization-tuned CNN classifier. The input thermal images are initially pre-processed to remove the noise present in them. After pre-processing, features like LBP, LDP, and LOOP are extracted. The collected features are then combined to produce a feature vector, which is the input to the proposed CNN classifier. Single hotspots, multiple hotspots, and string hotspots are the three types of faults that are supposed to be classified. After the classification process, the defects are located using the VGG-16\u00a0model. The weights of the CNN and VGG-16\u00a0models are modified using the proposed AqWH algorithm, which includes the distinctive characteristics of the wild horse and the Aquila search agents, to enhance classification and localization accuracy. The suggested possesses accuracy levels of 90% for classification and 96.11% for localization tasks, showing its superiority over conventional methods.<\/jats:p>","DOI":"10.3233\/idt-230631","type":"journal-article","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T16:18:18Z","timestamp":1708445898000},"page":"169-189","source":"Crossref","is-referenced-by-count":2,"title":["A novel technique for implementing hybrid optimization technique for PV thermal images to categorize and localize the faults"],"prefix":"10.1177","volume":"18","author":[{"given":"Ashwini","family":"Raorane","sequence":"first","affiliation":[{"name":"Department of Electronics, Ramrao Adik Institute of Technology, D. Y. Patil Deemed to be University, Navi Mumbai, Maharashtra, India"}]},{"given":"Dhiraj","family":"Magare","sequence":"additional","affiliation":[{"name":"Department of Electronics, Ramrao Adik Institute of Technology, Navi Mumbai, Maharashtra, India"}]},{"given":"Yogita","family":"Mistry","sequence":"additional","affiliation":[{"name":"Department of Electronics, Ramrao Adik Institute of Technology, Navi Mumbai, Maharashtra, India"}]}],"member":"179","reference":[{"issue":"24","key":"10.3233\/IDT-230631_ref1","doi-asserted-by":"crossref","first-page":"6496","DOI":"10.3390\/en13246496","article-title":"Automatic faults detection of photovoltaic farms: Solair, a deep learning-based system for thermal images","volume":"13","author":"Pierdicca","year":"2020","journal-title":"Energies"},{"issue":"5","key":"10.3233\/IDT-230631_ref2","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1016\/j.energy.2006.06.017","article-title":"An investigation of mismatch losses in solar photovoltaic cell networks","volume":"32","author":"Kaushika","year":"2007","journal-title":"Energy"},{"issue":"3","key":"10.3233\/IDT-230631_ref3","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.solmat.2005.04.022","article-title":"Experimental study of mismatch and shading effects in the I\u2013V characteristic of a photovoltaic module","volume":"90","author":"Alonso-Garcia","year":"2006","journal-title":"Solar Energy Materials and Solar Cells"},{"issue":"11","key":"10.3233\/IDT-230631_ref4","doi-asserted-by":"crossref","first-page":"3802","DOI":"10.3390\/app10113802","article-title":"Automatic detection system of deteriorated PV modules using drone with thermal camera","volume":"10","author":"Henry","year":"2020","journal-title":"Applied Sciences"},{"issue":"1","key":"10.3233\/IDT-230631_ref5","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1109\/TEC.2018.2873358","article-title":"Deep learning based module defect analysis for large-scale photovoltaic farms","volume":"34","author":"Li","year":"2018","journal-title":"IEEE Transactions On Energy Conversion"},{"key":"10.3233\/IDT-230631_ref6","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.isprsjprs.2018.04.010","article-title":"Photovoltaic panel extraction from very high-resolution aerial imagery using region\u2013line primitive association analysis and template matching","volume":"141","author":"Wang","year":"2018","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"10.3233\/IDT-230631_ref7","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1016\/j.rser.2016.04.079","article-title":"Faults and infrared thermographic diagnosis in operating c-Si photovoltaic modules: A review of research and future challenges","volume":"62","author":"Tsanakas","year":"2016","journal-title":"Renewable And Sustainable Energy Reviews"},{"key":"10.3233\/IDT-230631_ref8","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1016\/j.rser.2013.07.046","article-title":"Performance and degradation analysis for long term reliability of solar photovoltaic systems: A review","volume":"27","author":"Sharma","year":"2013","journal-title":"Renewable And Sustainable Energy Reviews"},{"key":"10.3233\/IDT-230631_ref9","first-page":"1783","article-title":"Fault diagnosis and classification of large-scale photovoltaic plants through aerial orthophoto thermal mapping","author":"Tsanakas","year":"2015","journal-title":"In Proceedings Of The 31\u00a0st European Photovoltaic Solar Energy Conference and Exhibition2015"},{"key":"10.3233\/IDT-230631_ref10","unstructured":"K\u00f6ntges M, Sarah K, Packard CE, Ulrike J, Karl AB. 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