Wind turbines anomaly detection based on power curves and ensemble learning-Reference-Cited by-同舟云学术

Wind turbines anomaly detection based on power curves and ensemble learning

Published:2020-12 Issue:19 Volume:14 Page:4086-4093
ISSN:1752-1416
Container-title:IET Renewable Power Generation
language:en
Short-container-title:IET Renewable Power Generation

Author:

Moreno Sinvaldo R.¹²,Coelho Leandro dos Santos¹³,Ayala Helon V.H.⁴^ORCID,Mariani Viviana Cocco¹²

Affiliation:

1. Department of Electrical EngineeringFederal University of Parana (UFPR)Rua Cel. Francisco Heráclito dos Santos, 10081531‐980CuritibaPRBrazil

2. Department of Mechanical EngineeringPontifical Catholic University of Parana (PUCPR)Rua Imaculada Conceição, 115580215‐901CuritibaPRBrazil

3. Industrial and Systems Engineering Graduate ProgramPontifical Catholic University of Parana (PUCPR)Rua Imaculada Conceição, 115580215‐901CuritibaPRBrazil

4. Department of Mechanical EngineeringPontifical Catholic University of Rio de Janeiro (PUC‐Rio)Rua Marques de São Vicente, 115522453‐900Rio de JaneiroRJBrazil

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Institution of Engineering and Technology (IET)

Subject

Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rpg.2020.0224

Reference49 articles.

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3. Wind turbines abnormality detection through analysis of wind farm power curves;Wang S.;Measurement,2016

4. The prediction and diagnosis of wind turbine faults;Kusiak A.;Renew. Energy,2011

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