Health-aware Model Predictive Control of Wind Turbines using Stifness Degradation Approach

Sanchez, Hector; Escobet, Teresa; Puig, Vicenç

doi:10.1016/j.ifacol.2020.12.2772

IFAC-PapersOnLine

2020

DOI: 10.1016/j.ifacol.2020.12.2772

|View full text |Cite

Health-aware Model Predictive Control of Wind Turbines using Stifness Degradation Approach

Hector Sanchez

Teresa Escobet

Vicenç Puig

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Comparative analysis of novel data‐driven techniques for remaining useful life estimation of wind turbine high‐speed shaft bearings

Pandit,

Santos,

Sierra‐García

2024

Energy Science & Engineering

View full text Add to dashboard Cite

As the global momentum for wind power generation accelerates, the industry faces substantial challenges due to premature failures in wind turbine components. These failures, particularly in critical elements like the high‐speed shaft bearing, lead to significant operational losses, including unplanned downtime and elevated maintenance costs. To mitigate these issues, it's crucial to have precise predictions of the remaining useful life (RUL) of these components, enabling timely interventions and more efficient maintenance schedules. This article proposes advanced, data‐driven approaches for estimating the RUL of wind turbine high‐speed shaft bearings, utilizing cutting‐edge techniques such as long short‐term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent units (GRU), and random forest (RF) algorithms. Our analysis leverages vibration data from a 2 MW wind turbine equipped with a 20‐tooth pinion gear, providing a thorough validation and comparison of these methodologies against traditional models. Our results reveal that the LSTM and BiLSTM models excel in both accuracy and computational efficiency for predicting RUL and enhancing system prognosis, surpassing the performance of conventional RF and GRU methods. This research underscores the potential of our innovative data‐driven strategies to develop effective RUL estimation algorithms, significantly advancing wind turbine proactive operation and maintenance operations.

show abstract

Comparative analysis of novel data‐driven techniques for remaining useful life estimation of wind turbine high‐speed shaft bearings

Pandit,

Santos,

Sierra‐García

2024

Energy Science & Engineering

View full text Add to dashboard Cite

show abstract

Gain-scheduling wind-turbine control to mitigate the effects of weather conditions on the drive-train degradation

2022

View full text Add to dashboard Cite

Evaluating the effect of control gains on wind turbine drive-train deterioration: An experimental study

Fandiño,

Martinez,

Dumon

et al. 2024

IFAC-PapersOnLine

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Health-aware Model Predictive Control of Wind Turbines using Stifness Degradation Approach

Cited by 3 publications

References 12 publications

Comparative analysis of novel data‐driven techniques for remaining useful life estimation of wind turbine high‐speed shaft bearings

Comparative analysis of novel data‐driven techniques for remaining useful life estimation of wind turbine high‐speed shaft bearings

Gain-scheduling wind-turbine control to mitigate the effects of weather conditions on the drive-train degradation

Evaluating the effect of control gains on wind turbine drive-train deterioration: An experimental study

Contact Info

Product

Resources

About