FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3014582166> ?p ?o ?g. }

• W3014582166 endingPage "887" @default.
• W3014582166 startingPage "878" @default.
• W3014582166 abstract "Electroluminescence (EL) imaging of photovoltiac (PV) modules offers high-speed, high-resolution information about device performance, affording opportunities for greater insight and efficiency in module characterization across manufacturing, research and development, and power plant operations and management. Predicting module electrical properties from EL image features is a critical step toward these applications. In this article, we demonstrate quantification of both generalized and performance mechanism-specific EL image features, using pixel intensity-based and machine learning classification algorithms. From EL image features, we build predictive models for PV module power and series resistance, using time-series current-voltage (I-V) and EL data obtained stepwise on five brands of modules spanning three Si cell types through two accelerated exposures: damp heat (DH) (85°C/85% RH) and thermal cycling (TC) (IEC 61215). In total, 195 pairs of EL images and I-V characteristics were analyzed, yielding 11700 individual PV cell images. A convolutional neural network was built to classify cells by the severity of busbar corrosion with high accuracy (95%). Generalized power predictive models estimated the maximum power of PV modules from EL images with high confidence and an adjusted-R <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> of 0.88, across all module brands and cell types in extended DH and TC exposures. Mechanistic degradation prediction was demonstrated by quantification of busbar corrosion in EL images of three module brands in DH, and subsequent modeling of series resistance using these mechanism-specific EL image features. For modules exhibiting busbar corrosion, we demonstrated series resistance predictive models with adjusted-R <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> of up to 0.73." @default.
• W3014582166 created "2020-04-10" @default.
• W3014582166 creator A5014974765 @default.
• W3014582166 creator A5018492285 @default.
• W3014582166 creator A5035558591 @default.
• W3014582166 creator A5038831556 @default.
• W3014582166 creator A5054989359 @default.
• W3014582166 creator A5085904418 @default.
• W3014582166 creator A5087506938 @default.
• W3014582166 date "2020-05-01" @default.
• W3014582166 modified "2023-10-10" @default.
• W3014582166 title "Generalized and Mechanistic PV Module Performance Prediction From Computer Vision and Machine Learning on Electroluminescence Images" @default.
• W3014582166 cites W2007345956 @default.
• W3014582166 cites W2017832357 @default.
• W3014582166 cites W205259702 @default.
• W3014582166 cites W2055815983 @default.
• W3014582166 cites W2065019325 @default.
• W3014582166 cites W2075693736 @default.
• W3014582166 cites W2078110295 @default.
• W3014582166 cites W2115804278 @default.
• W3014582166 cites W2129978592 @default.
• W3014582166 cites W2144354855 @default.
• W3014582166 cites W2156163116 @default.
• W3014582166 cites W2161663128 @default.
• W3014582166 cites W2166933282 @default.
• W3014582166 cites W2507112645 @default.
• W3014582166 cites W2515368710 @default.
• W3014582166 cites W2565516711 @default.
• W3014582166 cites W2582151580 @default.
• W3014582166 cites W2618530766 @default.
• W3014582166 cites W2886056699 @default.
• W3014582166 cites W2902366145 @default.
• W3014582166 cites W2902724425 @default.
• W3014582166 cites W2903183643 @default.
• W3014582166 cites W2903283628 @default.
• W3014582166 cites W2953947605 @default.
• W3014582166 cites W2964482767 @default.
• W3014582166 cites W3080572927 @default.
• W3014582166 doi "https://doi.org/10.1109/jphotov.2020.2973448" @default.
• W3014582166 hasPublicationYear "2020" @default.
• W3014582166 type Work @default.
• W3014582166 sameAs 3014582166 @default.
• W3014582166 citedByCount "30" @default.
• W3014582166 countsByYear W30145821662020 @default.
• W3014582166 countsByYear W30145821662021 @default.
• W3014582166 countsByYear W30145821662022 @default.
• W3014582166 countsByYear W30145821662023 @default.
• W3014582166 crossrefType "journal-article" @default.
• W3014582166 hasAuthorship W3014582166A5014974765 @default.
• W3014582166 hasAuthorship W3014582166A5018492285 @default.
• W3014582166 hasAuthorship W3014582166A5035558591 @default.
• W3014582166 hasAuthorship W3014582166A5038831556 @default.
• W3014582166 hasAuthorship W3014582166A5054989359 @default.
• W3014582166 hasAuthorship W3014582166A5085904418 @default.
• W3014582166 hasAuthorship W3014582166A5087506938 @default.
• W3014582166 hasBestOaLocation W30145821661 @default.
• W3014582166 hasConcept C11413529 @default.
• W3014582166 hasConcept C116615679 @default.
• W3014582166 hasConcept C119599485 @default.
• W3014582166 hasConcept C119857082 @default.
• W3014582166 hasConcept C121332964 @default.
• W3014582166 hasConcept C12267149 @default.
• W3014582166 hasConcept C127413603 @default.
• W3014582166 hasConcept C14485415 @default.
• W3014582166 hasConcept C154945302 @default.
• W3014582166 hasConcept C159985019 @default.
• W3014582166 hasConcept C160633673 @default.
• W3014582166 hasConcept C163258240 @default.
• W3014582166 hasConcept C165801399 @default.
• W3014582166 hasConcept C192562407 @default.
• W3014582166 hasConcept C192690417 @default.
• W3014582166 hasConcept C2779227376 @default.
• W3014582166 hasConcept C31625292 @default.
• W3014582166 hasConcept C41008148 @default.
• W3014582166 hasConcept C41291067 @default.
• W3014582166 hasConcept C62520636 @default.
• W3014582166 hasConceptScore W3014582166C11413529 @default.
• W3014582166 hasConceptScore W3014582166C116615679 @default.
• W3014582166 hasConceptScore W3014582166C119599485 @default.
• W3014582166 hasConceptScore W3014582166C119857082 @default.
• W3014582166 hasConceptScore W3014582166C121332964 @default.
• W3014582166 hasConceptScore W3014582166C12267149 @default.
• W3014582166 hasConceptScore W3014582166C127413603 @default.
• W3014582166 hasConceptScore W3014582166C14485415 @default.
• W3014582166 hasConceptScore W3014582166C154945302 @default.
• W3014582166 hasConceptScore W3014582166C159985019 @default.
• W3014582166 hasConceptScore W3014582166C160633673 @default.
• W3014582166 hasConceptScore W3014582166C163258240 @default.
• W3014582166 hasConceptScore W3014582166C165801399 @default.
• W3014582166 hasConceptScore W3014582166C192562407 @default.
• W3014582166 hasConceptScore W3014582166C192690417 @default.
• W3014582166 hasConceptScore W3014582166C2779227376 @default.
• W3014582166 hasConceptScore W3014582166C31625292 @default.
• W3014582166 hasConceptScore W3014582166C41008148 @default.
• W3014582166 hasConceptScore W3014582166C41291067 @default.
• W3014582166 hasConceptScore W3014582166C62520636 @default.
• W3014582166 hasFunder F4320306084 @default.
• W3014582166 hasFunder F4320308963 @default.

• next