Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis-Reference-Cited by-同舟云学术

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Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis

Published:2019-03 Issue: Volume:132 Page:1034-1048
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Jiménez Alfredo Arcos,García Márquez Fausto Pedro,Moraleda Victoria Borja,Gómez Muñoz Carlos Quiterio

Funder

Spanish Ministerio de Economía y Competitividad

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference75 articles.

1. International competitiveness of China's wind turbine manufacturing industry and implications for future development;Zhang;Renew. Sustain. Energy Rev.,2012

2. Optimal bidding strategy of wind power producers in pay-as-bid power markets;Afshar;Renew. Energy,2018

3. Wind energy harnessing of the nrel 5 mw reference wind turbine in icing conditions under different operational strategies;Zanon;Renew. Energy,2018

4. Iea Task 19: Cold Climate Wind Power Market Study 2015-2020;Karlsson,2017

5. Identification of critical components of wind turbines using fta over the time;Márquez;Renew. Energy,2016

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4. Non Invasive Fault Detection of Offshore Wind Turbines Using Deep Network-Based Thermogram Features;Arabian Journal for Science and Engineering;2024-06-26

5. A Novel Approach to Wind Turbine Blade Icing Detection With Limited Sensor Data via Spatiotemporal Attention Siamese Network;IEEE Transactions on Industrial Informatics;2024-06

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