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Towards improved solar PV module characterisation: Correlating electroluminescence image defects with I-V curve characteristics using a semantic segmentation based multi-defect detection algorithm

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dc.contributor.author Zandamela, Frank
dc.contributor.author Pratt, Lawrence E
dc.contributor.author May, Siyasanga I
dc.contributor.author Mkasi, Hlaluku W
dc.contributor.author Mabeo, Reuben T
dc.date.accessioned 2024-05-06T11:07:37Z
dc.date.available 2024-05-06T11:07:37Z
dc.date.issued 2023-11
dc.identifier.citation Zandamela, F., Pratt, L.E., May, S.I., Mkasi, H.W. & Mabeo, R.T. 2023. Towards improved solar PV module characterisation: Correlating electroluminescence image defects with I-V curve characteristics using a semantic segmentation based multi-defect detection algorithm. http://hdl.handle.net/10204/13668 . en_ZA
dc.identifier.isbn 978-0-7972-1907-6
dc.identifier.uri http://hdl.handle.net/10204/13668
dc.description.abstract There has been significant research on the relationship between current-voltage (I-V) curve characteristics and electroluminescence (EL) module defects. Current methods use EL image pixels to develop features, which are then correlated with module I-V curve characteristics. In most cases, image thresholding is used to gather pixel information. These approaches have two major limitations. First, they lack generalisability, as imaging conditions may vary from module to module, and thresholding algorithms are often developed for specific types of defects or imaging conditions. Second, the correlation between specific types of defects and I-V features cannot be studied because all defects are grouped into one highlevel defect detected by a sharp change in pixel intensity. In this paper, we conduct a correlation study between EL defects and IV curve characteristics of photovoltaic (PV) modules that were exposed to accelerated stress testing. We correlate power loss and two common EL defects. The defects are detected and quantified using a prediction model based on semantic segmentation in which each pixel is assigned to one of multiple classes. Results obtained indicate that the defect detection tool can be used to correlate power loss with dark cells and cell cracks. A significant amount of variability in output power delta can be explained by defects detected by the prediction model (r2= 72%). en_US
dc.format Fulltext en_US
dc.language.iso en en_US
dc.relation.uri https://events.saip.org.za/event/241/attachments/3495/5210/SASEC%2023%20Proceedings.pdf en_US
dc.source Southern African Sustainable Energy Conference (SASEC), Gqeberha, Port Elizabeth, 15-17 November 2023 en_US
dc.subject Cell cracks en_US
dc.subject Electroluminescence image defect detection en_US
dc.subject I-V curve characteristics en_US
dc.subject Deep learning en_US
dc.subject PV module en_US
dc.subject Semantic segmentation en_US
dc.title Towards improved solar PV module characterisation: Correlating electroluminescence image defects with I-V curve characteristics using a semantic segmentation based multi-defect detection algorithm en_US
dc.type Conference Presentation en_US
dc.description.pages 120-126 en_US
dc.description.note Paper presented at the Southern African Sustainable Energy Conference (SASEC), Gqeberha, Port Elizabeth, 15-17 November 2023. en_US
dc.description.cluster Smart Places en_US
dc.description.impactarea Living Energy Lab Platform en_US
dc.description.impactarea Energy Supply and Demand en_US
dc.identifier.apacitation Zandamela, F., Pratt, L. E., May, S. I., Mkasi, H. W., & Mabeo, R. T. (2023). Towards improved solar PV module characterisation: Correlating electroluminescence image defects with I-V curve characteristics using a semantic segmentation based multi-defect detection algorithm. http://hdl.handle.net/10204/13668 en_ZA
dc.identifier.chicagocitation Zandamela, Frank, Lawrence E Pratt, Siyasanga I May, Hlaluku W Mkasi, and Reuben T Mabeo. "Towards improved solar PV module characterisation: Correlating electroluminescence image defects with I-V curve characteristics using a semantic segmentation based multi-defect detection algorithm." <i>Southern African Sustainable Energy Conference (SASEC), Gqeberha, Port Elizabeth, 15-17 November 2023</i> (2023): http://hdl.handle.net/10204/13668 en_ZA
dc.identifier.vancouvercitation Zandamela F, Pratt LE, May SI, Mkasi HW, Mabeo RT, Towards improved solar PV module characterisation: Correlating electroluminescence image defects with I-V curve characteristics using a semantic segmentation based multi-defect detection algorithm; 2023. http://hdl.handle.net/10204/13668 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Zandamela, Frank AU - Pratt, Lawrence E AU - May, Siyasanga I AU - Mkasi, Hlaluku W AU - Mabeo, Reuben T AB - There has been significant research on the relationship between current-voltage (I-V) curve characteristics and electroluminescence (EL) module defects. Current methods use EL image pixels to develop features, which are then correlated with module I-V curve characteristics. In most cases, image thresholding is used to gather pixel information. These approaches have two major limitations. First, they lack generalisability, as imaging conditions may vary from module to module, and thresholding algorithms are often developed for specific types of defects or imaging conditions. Second, the correlation between specific types of defects and I-V features cannot be studied because all defects are grouped into one highlevel defect detected by a sharp change in pixel intensity. In this paper, we conduct a correlation study between EL defects and IV curve characteristics of photovoltaic (PV) modules that were exposed to accelerated stress testing. We correlate power loss and two common EL defects. The defects are detected and quantified using a prediction model based on semantic segmentation in which each pixel is assigned to one of multiple classes. Results obtained indicate that the defect detection tool can be used to correlate power loss with dark cells and cell cracks. A significant amount of variability in output power delta can be explained by defects detected by the prediction model (r2= 72%). DA - 2023-11 DB - ResearchSpace DP - CSIR J1 - Southern African Sustainable Energy Conference (SASEC), Gqeberha, Port Elizabeth, 15-17 November 2023 KW - Cell cracks KW - Electroluminescence image defect detection KW - I-V curve characteristics KW - Deep learning KW - PV module KW - Semantic segmentation LK - https://researchspace.csir.co.za PY - 2023 SM - 978-0-7972-1907-6 T1 - Towards improved solar PV module characterisation: Correlating electroluminescence image defects with I-V curve characteristics using a semantic segmentation based multi-defect detection algorithm TI - Towards improved solar PV module characterisation: Correlating electroluminescence image defects with I-V curve characteristics using a semantic segmentation based multi-defect detection algorithm UR - http://hdl.handle.net/10204/13668 ER - en_ZA
dc.identifier.worklist 27605 en_US


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