Detection of Wind Turbine Failures through Cross-Information between Neighbouring Turbines

Autor: Pere Marti-Puig, Jordi Cusidó, Francisco J. Lozano, Moises Serra-Serra, Cesar F. Caiafa, Jordi Solé-Casals

Publikováno v: Applied Sciences, Vol 12, Iss 19, p 9491 (2022)

In this paper, the time variation of signals from several SCADA systems of geographically closed turbines are analysed and compared. When operating correctly, they show a clear pattern of joint variation. However, the presence of a failure in one of

Externí odkaz: https://doaj.org/article/cea359fa1387472eb56e8afd2328f127

Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines

Autor: Pere Marti-Puig, Alejandro Blanco-M., Moisès Serra-Serra, Jordi Solé-Casals

Publikováno v: Applied Sciences, Vol 11, Iss 2, p 590 (2021)

In this paper, a method to build models to monitor and evaluate the health status of wind turbines using Single-hidden Layer Feedforward Neural networks (SLFN) is presented. The models are trained using the Extreme Learning Machines (ELM) strategy. T

Externí odkaz: https://doaj.org/article/2851c5ced0084e80b6ee8841ead4f9c1

Exploring the Effect of Temporal Aggregation on SCADA Data for Wind Turbine Prognosis Using a Normality Model

Autor: Alejandro Bennásar-Sevillá, Pere Marti-Puig, Jordi Solé-Casals, Alejandro Blanco-M.

Publikováno v: Applied Sciences, Vol 11, Iss 6405, p 6405 (2021)
O2, repositorio institucional de la UOC
Universitat Oberta de Catalunya (UOC)
Applied Sciences
Volume 11
Issue 14

Today, the use of SCADA data for predictive maintenance and forecasting of wind turbines in wind farms is gaining popularity due to the low cost of this solution compared to others that require the installation of additional equipment. SCADA data pro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8653e1ef00cd3b1a372bac3fc4ea0839
https://www.mdpi.com/2076-3417/11/14/6405

Increasing transparency and robustness of machine learning methods for SCADA based condition monitoring in wind turbines

Autor: Letzgus, Simon

SCADA data analytics has attracted considerable research interest for monitoring wind turbine condition without additional equipment. Above all, normal behaviour models using artificial neural networks (ANNs) have shown promising results. However, th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bdcf7f2dcd39c14b877d61ee7c90eb17

Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines

Autor: Jordi Solé-Casals, Alejandro Blanco-M., Pere Marti-Puig, Moisès Serra-Serra

Publikováno v: Applied Sciences
Volume 11
Issue 2
Applied Sciences, Vol 11, Iss 590, p 590 (2021)

In this paper, a method to build models to monitor and evaluate the health status of wind turbines using Single-hidden Layer Feedforward Neural networks (SLFN) is presented. The models are trained using the Extreme Learning Machines (ELM) strategy. T

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6050f97b52a5c6c4cf07dd0c6b509924
https://doi.org/10.3390/app11020590