Matches in SemOpenAlex for { <https://semopenalex.org/work/W3196343801> ?p ?o ?g. }
Showing items 1 to 88 of
88
with 100 items per page.
- W3196343801 abstract "This paper develops a novel self-training U-net (STU-net) based method for the automated WPC model generation without requiring data pre-processing. The self-training (ST) process of STU-net has two steps. First, different from traditional studies regarding the WPC modeling as a curve fitting problem, in this paper, we renovate the WPC modeling formulation from a machine vision aspect. To develop sufficiently diversified training samples, we synthesize supervisory control and data acquisition (SCADA) data based on a set of S-shape functions depicting WPCs. These synthesized SCADA data and WPC functions are visualized as images and paired as training samples(I_x, I_wpc). A U-net is then developed to approximate the model recovering I_wpc from I_x. The developed U-net is applied into observed SCADA data and can successfully generate the I_wpc. Moreover, we develop a pixel mapping and correction process to derive a mathematical form f_wpc representing I_wpcgenerated previously. The proposed STU-net only needs to train once and does not require any data preprocessing in applications. Numerical experiments based on 76 WTs are conducted to validate the superiority of the proposed method by benchmarking against classical WPC modeling methods. To demonstrate the repeatability of the presented research, we release our code at https://github.com/IkeYang/STU-net." @default.
- W3196343801 created "2021-09-13" @default.
- W3196343801 creator A5022198507 @default.
- W3196343801 creator A5045545636 @default.
- W3196343801 creator A5082849266 @default.
- W3196343801 date "2021-08-19" @default.
- W3196343801 modified "2023-09-27" @default.
- W3196343801 title "Generative Wind Power Curve Modeling Via Machine Vision: A Self-learning Deep Convolutional Network Based Method" @default.
- W3196343801 cites W180771247 @default.
- W3196343801 cites W1836465849 @default.
- W3196343801 cites W1901129140 @default.
- W3196343801 cites W1976854751 @default.
- W3196343801 cites W1982870982 @default.
- W3196343801 cites W1985255373 @default.
- W3196343801 cites W1987092188 @default.
- W3196343801 cites W2024027437 @default.
- W3196343801 cites W2044963328 @default.
- W3196343801 cites W2075672667 @default.
- W3196343801 cites W2097466372 @default.
- W3196343801 cites W2098854737 @default.
- W3196343801 cites W2116224582 @default.
- W3196343801 cites W2116540796 @default.
- W3196343801 cites W2169461337 @default.
- W3196343801 cites W2202172816 @default.
- W3196343801 cites W2326041979 @default.
- W3196343801 cites W2416349653 @default.
- W3196343801 cites W2476145847 @default.
- W3196343801 cites W2538056815 @default.
- W3196343801 cites W2556394974 @default.
- W3196343801 cites W2559782006 @default.
- W3196343801 cites W2719445145 @default.
- W3196343801 cites W2794466895 @default.
- W3196343801 cites W2942536656 @default.
- W3196343801 cites W2981208062 @default.
- W3196343801 cites W3112611640 @default.
- W3196343801 doi "https://doi.org/10.48550/arxiv.2109.00894" @default.
- W3196343801 hasPublicationYear "2021" @default.
- W3196343801 type Work @default.
- W3196343801 sameAs 3196343801 @default.
- W3196343801 citedByCount "0" @default.
- W3196343801 crossrefType "posted-content" @default.
- W3196343801 hasAuthorship W3196343801A5022198507 @default.
- W3196343801 hasAuthorship W3196343801A5045545636 @default.
- W3196343801 hasAuthorship W3196343801A5082849266 @default.
- W3196343801 hasBestOaLocation W31963438011 @default.
- W3196343801 hasConcept C111919701 @default.
- W3196343801 hasConcept C113863187 @default.
- W3196343801 hasConcept C119599485 @default.
- W3196343801 hasConcept C119857082 @default.
- W3196343801 hasConcept C124101348 @default.
- W3196343801 hasConcept C127413603 @default.
- W3196343801 hasConcept C154945302 @default.
- W3196343801 hasConcept C160633673 @default.
- W3196343801 hasConcept C177264268 @default.
- W3196343801 hasConcept C199360897 @default.
- W3196343801 hasConcept C34736171 @default.
- W3196343801 hasConcept C41008148 @default.
- W3196343801 hasConcept C98045186 @default.
- W3196343801 hasConceptScore W3196343801C111919701 @default.
- W3196343801 hasConceptScore W3196343801C113863187 @default.
- W3196343801 hasConceptScore W3196343801C119599485 @default.
- W3196343801 hasConceptScore W3196343801C119857082 @default.
- W3196343801 hasConceptScore W3196343801C124101348 @default.
- W3196343801 hasConceptScore W3196343801C127413603 @default.
- W3196343801 hasConceptScore W3196343801C154945302 @default.
- W3196343801 hasConceptScore W3196343801C160633673 @default.
- W3196343801 hasConceptScore W3196343801C177264268 @default.
- W3196343801 hasConceptScore W3196343801C199360897 @default.
- W3196343801 hasConceptScore W3196343801C34736171 @default.
- W3196343801 hasConceptScore W3196343801C41008148 @default.
- W3196343801 hasConceptScore W3196343801C98045186 @default.
- W3196343801 hasLocation W31963438011 @default.
- W3196343801 hasOpenAccess W3196343801 @default.
- W3196343801 hasPrimaryLocation W31963438011 @default.
- W3196343801 hasRelatedWork W2077451083 @default.
- W3196343801 hasRelatedWork W2235797036 @default.
- W3196343801 hasRelatedWork W2382928216 @default.
- W3196343801 hasRelatedWork W2383487638 @default.
- W3196343801 hasRelatedWork W2889453578 @default.
- W3196343801 hasRelatedWork W2961085424 @default.
- W3196343801 hasRelatedWork W4306674287 @default.
- W3196343801 hasRelatedWork W4327738674 @default.
- W3196343801 hasRelatedWork W811092902 @default.
- W3196343801 hasRelatedWork W962149676 @default.
- W3196343801 isParatext "false" @default.
- W3196343801 isRetracted "false" @default.
- W3196343801 magId "3196343801" @default.
- W3196343801 workType "article" @default.