FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4225523356> ?p ?o ?g. }

• W4225523356 endingPage "5015" @default.
• W4225523356 startingPage "5005" @default.
• W4225523356 abstract "With the continuously increasing application of photovoltaic (PV) panels, how to effectively manage these valuable facilities has become an issue of concern. To date, some methods have been developed to meet this purpose. However, to date, a satisfactory solution has not been achieved for managing large-scale solar PV power plants. To address this issue, a new PV panel condition monitoring and fault diagnosis technique is developed in this paper. The new technique uses a U-Net neural network and a classifier in combination to intelligently analyse the PV panel’s infrared thermal images taken by drones or other kinds of remote operating systems. The novelties of this research include: (1) a U-net neural network is developed and trained to carry out image segmentation, thereby significantly improving the efficiency of image processing; (2) the condition monitoring and fault diagnosis is based on the contour features in the ‘mask’ of true colour infrared images. As compared to the true colour images, their mask images have little interference information, the reliability and accuracy of condition monitoring and fault diagnosis based on mask images can be guaranteed to a large extent. In the research, 295 infrared images were taken first from the PV panels in different health states, and then their ‘masks’ were manually created using the software LabelMe. Secondly, enlarge the number of image samples using three image expansion methods (i.e. mirroring left and right, flipping up and down, and cropping and then zooming in) to establish the image sample database for training and testing the U-net neural network. Thirdly, use 1852 infrared images and their mask images stored in the database to train a U-Net neural network until the trained U-net neural network can create mask images as accurately as the software LabelMe does. The trained neural network will guarantee that the achieved segmentation accuracy can be as high as 95.2%. Finally, four potential criteria (i.e., Contour area, Perimeter, Aspect ratio and Ratio of contour area to the area of contour outer rectangle) are proposed to characterise the contour of mask images and then based on the calculation results of these four criteria, different types of PV panel faults are diagnosed with the aid of three kinds of classifiers. The classifiers are Decision tree, K-Nearest Neighbours algorithm (KNN), and Support-vector machine (SVM). The research results have shown that the combined use of a well-trained U-Net neural network and Decision tree can diagnose the PV panel faults with 99.8% accuracy. Therefore, it may arguably provide a promising intelligent tool for condition monitoring the PV panels." @default.
• W4225523356 created "2022-05-05" @default.
• W4225523356 creator A5017943288 @default.
• W4225523356 creator A5025261785 @default.
• W4225523356 creator A5029851581 @default.
• W4225523356 creator A5048221694 @default.
• W4225523356 creator A5050388284 @default.
• W4225523356 creator A5083490016 @default.
• W4225523356 date "2022-11-01" @default.
• W4225523356 modified "2023-10-18" @default.
• W4225523356 title "Intelligent monitoring of photovoltaic panels based on infrared detection" @default.
• W4225523356 cites W2379354547 @default.
• W4225523356 cites W2560913958 @default.
• W4225523356 cites W2576020174 @default.
• W4225523356 cites W2606436201 @default.
• W4225523356 cites W2619173211 @default.
• W4225523356 cites W2753383192 @default.
• W4225523356 cites W2773871505 @default.
• W4225523356 cites W2789878874 @default.
• W4225523356 cites W2885836748 @default.
• W4225523356 cites W2895159086 @default.
• W4225523356 cites W2912146532 @default.
• W4225523356 cites W2920901391 @default.
• W4225523356 cites W2963150697 @default.
• W4225523356 cites W2965643259 @default.
• W4225523356 cites W2980326480 @default.
• W4225523356 cites W2994825132 @default.
• W4225523356 cites W3003936655 @default.
• W4225523356 cites W3011781975 @default.
• W4225523356 cites W3037772610 @default.
• W4225523356 cites W3046960025 @default.
• W4225523356 cites W3070616691 @default.
• W4225523356 cites W3093073737 @default.
• W4225523356 cites W3165505571 @default.
• W4225523356 cites W3203727370 @default.
• W4225523356 cites W4207024474 @default.
• W4225523356 doi "https://doi.org/10.1016/j.egyr.2022.03.173" @default.
• W4225523356 hasPublicationYear "2022" @default.
• W4225523356 type Work @default.
• W4225523356 citedByCount "12" @default.
• W4225523356 countsByYear W42255233562022 @default.
• W4225523356 countsByYear W42255233562023 @default.
• W4225523356 crossrefType "journal-article" @default.
• W4225523356 hasAuthorship W4225523356A5017943288 @default.
• W4225523356 hasAuthorship W4225523356A5025261785 @default.
• W4225523356 hasAuthorship W4225523356A5029851581 @default.
• W4225523356 hasAuthorship W4225523356A5048221694 @default.
• W4225523356 hasAuthorship W4225523356A5050388284 @default.
• W4225523356 hasAuthorship W4225523356A5083490016 @default.
• W4225523356 hasBestOaLocation W42255233561 @default.
• W4225523356 hasConcept C115961682 @default.
• W4225523356 hasConcept C119599485 @default.
• W4225523356 hasConcept C124913957 @default.
• W4225523356 hasConcept C127313418 @default.
• W4225523356 hasConcept C127413603 @default.
• W4225523356 hasConcept C152745839 @default.
• W4225523356 hasConcept C15336307 @default.
• W4225523356 hasConcept C154945302 @default.
• W4225523356 hasConcept C165205528 @default.
• W4225523356 hasConcept C172707124 @default.
• W4225523356 hasConcept C175551986 @default.
• W4225523356 hasConcept C2775846686 @default.
• W4225523356 hasConcept C31972630 @default.
• W4225523356 hasConcept C41008148 @default.
• W4225523356 hasConcept C41291067 @default.
• W4225523356 hasConcept C50644808 @default.
• W4225523356 hasConcept C78762247 @default.
• W4225523356 hasConcept C89600930 @default.
• W4225523356 hasConcept C9417928 @default.
• W4225523356 hasConceptScore W4225523356C115961682 @default.
• W4225523356 hasConceptScore W4225523356C119599485 @default.
• W4225523356 hasConceptScore W4225523356C124913957 @default.
• W4225523356 hasConceptScore W4225523356C127313418 @default.
• W4225523356 hasConceptScore W4225523356C127413603 @default.
• W4225523356 hasConceptScore W4225523356C152745839 @default.
• W4225523356 hasConceptScore W4225523356C15336307 @default.
• W4225523356 hasConceptScore W4225523356C154945302 @default.
• W4225523356 hasConceptScore W4225523356C165205528 @default.
• W4225523356 hasConceptScore W4225523356C172707124 @default.
• W4225523356 hasConceptScore W4225523356C175551986 @default.
• W4225523356 hasConceptScore W4225523356C2775846686 @default.
• W4225523356 hasConceptScore W4225523356C31972630 @default.
• W4225523356 hasConceptScore W4225523356C41008148 @default.
• W4225523356 hasConceptScore W4225523356C41291067 @default.
• W4225523356 hasConceptScore W4225523356C50644808 @default.
• W4225523356 hasConceptScore W4225523356C78762247 @default.
• W4225523356 hasConceptScore W4225523356C89600930 @default.
• W4225523356 hasConceptScore W4225523356C9417928 @default.
• W4225523356 hasLocation W42255233561 @default.
• W4225523356 hasLocation W42255233562 @default.
• W4225523356 hasOpenAccess W4225523356 @default.
• W4225523356 hasPrimaryLocation W42255233561 @default.
• W4225523356 hasRelatedWork W1669643531 @default.
• W4225523356 hasRelatedWork W1990796262 @default.
• W4225523356 hasRelatedWork W2005437358 @default.
• W4225523356 hasRelatedWork W2008656436 @default.
• W4225523356 hasRelatedWork W2023558673 @default.
• W4225523356 hasRelatedWork W2110230079 @default.

• next