[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125176-en":3,"doc-seo-125176-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125176,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning Assisted Analysis of Solar Module Degradation","Solar modules in utility-scale photovoltaic systems are expected to deliver decades of service, yet field exposure to ultraviolet radiation, temperature swings, and mechanical loading drives multiple degradation pathways and reduces durability. Cyclic loads from coupled deformation and thermal variation are identified as dominant stressors over operational lifetimes. However, routine electroluminescence (EL) imaging can produce millions of measurements and the module is a multivariable material system, making manual inspection and comprehensive optimization infeasible. This dissertation uses machine learning to (1) automatically quantify defects from EL images and (2) link bill-of-materials to durability, enabling improved inspection and material selection.","UC Berkeley  \nUC Berkeley Electronic Theses and Dissertations  \nTitle  \nMachine Learning Assisted Analysis of Solar Module Degradation  \nPermalink  \n[https://escholarship.org/uc/item/86c4g4k0](https://escholarship.org/uc/item/86c4g4k0)  \nAuthor  \nChen, Xin  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nMachine Learning Assisted Analysis of Solar Module Degradation  \nBy  \nXin Chen  \nA dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy  \nin  \nEngineering-Materials Science and Engineering  \nin the  \nGraduate Division  \nof the  \nUniversity of California, Berkeley  \nCommittee in charge:  \nDr Anubhav Jain, Co-Chair Professor Gerbrand Ceder, Co-Chair Professor Mark Asta  \nProfessor Grace Gu  \nFall 2024  \nMachine Learning Assisted Analysis of Solar Module Degradation  \nCopyright 2024  \nby  \nXin Chen  \n1  \nAbstract  \nMachine Learning Assisted Analysis of Solar Module Degradation  \nby  \nXin Chen  \nDoctor of Philosophy in Engineering-Materials Science and Engineering University of California, Berkeley  \nDr Anubhav Jain, Co-Chair  \nProfessor Gerbrand Ceder, Co-Chair  \nSolar modules in utility-scale PV systems are expected to have decades of lifetime to effectively rival the cost of conventional energy sources. These modules installed in the field are subject to multiple environmental stresses such as ultraviolet light, temperature variation, and mechanical loading induced by snow, wind, or hail. These factors introduce multiple pathways of degradation, reducing the durability of the module. Among all these degradation factors, the cyclic loads resulting from mechanical deformation and temperature variation are some of the dominant stresses that cause degradation throughout the operational lifetime of PV modules. To achieve the goal of long-time operation, it is essential to implement timely fault detection of field modules as well as to optimize the durability of materials used in solar modules. However, field surveys of solar farms that normally use electroluminescence (EL) imaging characterization can generate millions of measurements. Such a voluminous data set makes it infeasible to analyze by rote inspection. In addition, a typical solar module is a multivariable material system which complicates the task of a comprehensive optimization of the module design.  \nTherefore, this dissertation seeks to address these challenges using machine learning methods through two primary objectives: (1) to quantitatively characterize the material defects such as cracks or solder breakage, and understand crack effects on module degradation by automatically analyzing electroluminescence (EL) images, and (2) to investigate the impacts of the module’s bill-of-materials (BOM) on its durability, providing insight into improving material selection and module design.  \nEL imaging is a fast and non-destructive characterization method to inspect solar modulesand identify defective cells at macro-scale level. The images often contain complex background, making it difficult to separate target solar cells from the background or extract quantitative features of defects such as cracks for further investigation. To address these  \n2  \nchallenges, we enhance the EL image analysis methods (Chapter 2) by improving background elimination, module alignment, and cell segmentation. These refinements lay the groundwork for comprehensive EL image processing in subsequent chapters, enabling deeper investigation into the quantitative characterization of defects and their impact on module degradation.  \nManual inspection of EL images in the field is challenging due to the large volume of modulesand the complexity of the defects. Using the aforementioned preprocessing methods and an object detection pipeline designed in Chapter 3, we facilitate the systematic characterization of various types of modul","cbCaid6hE98hYAfX","https://ap.wps.com/l/cbCaid6hE98hYAfX","pdf",33638887,1,142,"English","en",105,"# Abstract\n## Background and problem statement\n## Objectives and approach\n## EL image preprocessing and automation\n## Crack characterization and correlation with power loss\n## Implications for material selection and design","[{\"question\":\"Why is solar module degradation difficult to analyze in the field?\",\"answer\":\"Field surveys using electroluminescence (EL) imaging generate millions of measurements, making rote manual inspection infeasible. The module is also a multivariable material system that complicates comprehensive optimization.\"},{\"question\":\"What is the main purpose of using machine learning in this dissertation?\",\"answer\":\"The work targets two goals: automatically quantify material defects such as cracks or solder breakage from EL images, and evaluate how the module bill-of-materials (BOM) influences durability.\"},{\"question\":\"How do the results connect cracks to module performance?\",\"answer\":\"A semantic segmentation model extracts cracks from EL images and produces quantified descriptors. Crack features show a strong correlation with module power loss, with an average Spearman correlation coefficient around 0.65.\"}]","Machine Learning Assisted Analysis of Solar Module Degradation | PDF",1785897219,358,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-assisted-analysis-of-solar-module-degradation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-assisted-analysis-of-solar-module-degradation/125176/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is solar module degradation difficult to analyze in the field?","Question",{"text":75,"@type":76},"Field surveys using electroluminescence (EL) imaging generate millions of measurements, making rote manual inspection infeasible. The module is also a multivariable material system that complicates comprehensive optimization.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the main purpose of using machine learning in this dissertation?",{"text":80,"@type":76},"The work targets two goals: automatically quantify material defects such as cracks or solder breakage from EL images, and evaluate how the module bill-of-materials (BOM) influences durability.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the results connect cracks to module performance?",{"text":84,"@type":76},"A semantic segmentation model extracts cracks from EL images and produces quantified descriptors. Crack features show a strong correlation with module power loss, with an average Spearman correlation coefficient around 0.65.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]