This article presents a novel methodology to detect missing strings in very-large-scale photovoltaic (VLSPV) systems, utilizing only data acquired at the stringbox level. Leveraging data analysis and unsupervised machine learning techniques, the proposed method estimates the quantity of missing strings per stringbox by comparing the direct current from each stringbox with neighboring stringboxes within the same region. The approach addresses a gap in the existing literature by providing a solution tailored to the typical instrumentation level of VLSPV plants. The work encompasses an analysis of the energy losses caused by missing strings, quantifying the impact on the overall system performance. Evaluation against real-world data showed a precision of around 90% of the proposed method in detecting missing strings. The findings offer valuable insights for operations and maintenance teams, enabling identification and mitigation of problematic strings in VLSPV plants.
{"title":"A Method for the Estimation of Missing Strings in Very-Large-Scale Photovoltaic Power Plants","authors":"Tiago Edmir Simão;Bruno Castro Valle;Yago Castro Rosa;Fernando Santos Varela;Arliones Hoeller;Mario de Noronha Neto;Carlos Ernani Fries;Richard Demo Souza","doi":"10.1109/JPHOTOV.2024.3430977","DOIUrl":"10.1109/JPHOTOV.2024.3430977","url":null,"abstract":"This article presents a novel methodology to detect missing strings in very-large-scale photovoltaic (VLSPV) systems, utilizing only data acquired at the stringbox level. Leveraging data analysis and unsupervised machine learning techniques, the proposed method estimates the quantity of missing strings per stringbox by comparing the direct current from each stringbox with neighboring stringboxes within the same region. The approach addresses a gap in the existing literature by providing a solution tailored to the typical instrumentation level of VLSPV plants. The work encompasses an analysis of the energy losses caused by missing strings, quantifying the impact on the overall system performance. Evaluation against real-world data showed a precision of around 90% of the proposed method in detecting missing strings. The findings offer valuable insights for operations and maintenance teams, enabling identification and mitigation of problematic strings in VLSPV plants.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"14 5","pages":"839-847"},"PeriodicalIF":2.5,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141864222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research explores numerical modeling and simulation studies of a lead-free perovskite solar cell employing (Cs2AgBi0.75Sb0.25Br6) as the absorber layer and utilizing single-walled carbon nanotubes (SWCNTs) in conjunction with metal oxides as the electron transport layer (ETL). Systematic investigation with six different carrier transport layers (both ETL and hole transport layer) along with comprehensive exploration of device physics, coupled with diverse optimization strategies concerning thickness, bandgap, and defect density (both interfacial and bulk), has been carried out. Our study reveals that the proposed configuration can achieve a remarkable device performance, approaching 29.06% efficiency with a current density of 35 mA/cm 2