IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i7p520-d73353.html
   My bibliography  Save this article

On Real-Time Fault Detection in Wind Turbines: Sensor Selection Algorithm and Detection Time Reduction Analysis

Author

Listed:
  • Francesc Pozo

    (Control, Dynamics and Applications (CoDAlab), Departament de Matemàtiques, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 6–12, Sant Adrià de Besòs (Barcelona) 08930, Spain)

  • Yolanda Vidal

    (Control, Dynamics and Applications (CoDAlab), Departament de Matemàtiques, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 6–12, Sant Adrià de Besòs (Barcelona) 08930, Spain)

  • Josep M. Serrahima

    (Control, Dynamics and Applications (CoDAlab), Departament de Matemàtiques, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 6–12, Sant Adrià de Besòs (Barcelona) 08930, Spain)

Abstract

In this paper, we address the problem of real-time fault detection in wind turbines. Starting from a data-driven fault detection method, the contribution of this paper is twofold. First, a sensor selection algorithm is proposed with the goal to reduce the computational effort of the fault detection method. Second, an analysis is performed to reduce the data acquisition time needed by the fault detection method, that is, with the goal of reducing the fault detection time. The proposed methods are tested in a benchmark wind turbine where different actuator and sensor failures are simulated. The results demonstrate the performance and effectiveness of the proposed algorithms that dramatically reduce the number of sensors and the fault detection time.

Suggested Citation

  • Francesc Pozo & Yolanda Vidal & Josep M. Serrahima, 2016. "On Real-Time Fault Detection in Wind Turbines: Sensor Selection Algorithm and Detection Time Reduction Analysis," Energies, MDPI, vol. 9(7), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:7:p:520-:d:73353
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/7/520/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/9/7/520/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. I. T. Jolliffe, 1972. "Discarding Variables in a Principal Component Analysis. I: Artificial Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(2), pages 160-173, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. José Ramón del Álamo Salgado & Mario J. Durán Martínez & Francisco J. Muñoz Gutiérrez & Jorge Alarcon, 2021. "Analysis of the Gearbox Oil Maintenance Procedures in Wind Energy II," Energies, MDPI, vol. 14(12), pages 1-18, June.
    2. Francesc Pozo & Yolanda Vidal & Óscar Salgado, 2018. "Wind Turbine Condition Monitoring Strategy through Multiway PCA and Multivariate Inference," Energies, MDPI, vol. 11(4), pages 1-19, March.
    3. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
    4. Estefania Artigao & Sofia Koukoura & Andrés Honrubia-Escribano & James Carroll & Alasdair McDonald & Emilio Gómez-Lázaro, 2018. "Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train," Energies, MDPI, vol. 11(4), pages 1-18, April.
    5. Cristian Velandia-Cardenas & Yolanda Vidal & Francesc Pozo, 2021. "Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data," Energies, MDPI, vol. 14(6), pages 1-26, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hertrich Markus, 2019. "A Novel Housing Price Misalignment Indicator for Germany," German Economic Review, De Gruyter, vol. 20(4), pages 759-794, December.
    2. Colosimo Bianca Maria & Moya Ester Gutierrez & Moroni Giovanni & Petrò Stefano, 2008. "Statistical Sampling Strategies for Geometric Tolerance Inspection by CMM," Stochastics and Quality Control, De Gruyter, vol. 23(1), pages 109-121, January.
    3. Sürücü, Lütfi & YIKILMAZ, İbrahim & MASLAKÇI, Ahmet, 2022. "Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations," OSF Preprints fgd4e, Center for Open Science.
    4. Hatem Jemmali & Mohamed Salah Matoussi, 2012. "A Multidimensional Analysis of Water Poverty at A Local Scale- Application of Improved Water Poverty Index for Tunisia," Working Papers 730, Economic Research Forum, revised 2012.
    5. Gweneth Leigh & Milica Muminovic & Rachel Davey, 2023. "Enjoyed by Jack but Endured by Jill: An Exploratory Case Study Examining Differences in Adolescent Design Preferences and Perceived Impacts of a Secondary Schoolyard," IJERPH, MDPI, vol. 20(5), pages 1-14, February.
    6. Pacheco, Joaquín & Casado, Silvia & Porras, Santiago, 2013. "Exact methods for variable selection in principal component analysis: Guide functions and pre-selection," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 95-111.
    7. Jérome SARACCO & Marie CHAVENT & Vanessa KUENTZ, 2010. "Clustering of categorical variables around latent variables," Cahiers du GREThA (2007-2019) 2010-02, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    8. Huseyin Aytug & Siong Hook Law & Nirvikar Singh, 2018. "What Can We Learn from Global and Regional Rankings of Countries?," Millennial Asia, , vol. 9(2), pages 119-139, August.
    9. Psaradakis, Zacharias & Vávra, Marián, 2014. "On testing for nonlinearity in multivariate time series," Economics Letters, Elsevier, vol. 125(1), pages 1-4.
    10. Serena Ng, 2017. "Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data," NBER Working Papers 23673, National Bureau of Economic Research, Inc.
    11. Oliveira, Paulo Ricardo Silva & Silveira, José Maria Ferreira Jardim da & Magalhães, Marcelo Marques de & Souza, Roney Fraga, 2020. "International trade in GMOs: have markets paid premiums on Brazilian soybeans?," Revista de Economia e Sociologia Rural (RESR), Sociedade Brasileira de Economia e Sociologia Rural, vol. 58(1), January.
    12. Gary T. Henry & James H. McMillan, 1993. "Performance Data," Evaluation Review, , vol. 17(6), pages 643-652, December.
    13. Brint, Andrew & Genovese, Andrea & Piccolo, Carmela & Taboada-Perez, Gerardo J., 2021. "Reducing data requirements when selecting key performance indicators for supply chain management: The case of a multinational automotive component manufacturer," International Journal of Production Economics, Elsevier, vol. 233(C).
    14. Pereira, Guillermo Ivan & Pereira da Silva, Patrícia & Cerqueira, Pedro André, 2020. "Electricity distribution incumbents' adaptation toward decarbonized and smarter grids: Evidence on the role market, regulatory, investment, and firm-level factors," Energy Policy, Elsevier, vol. 142(C).
    15. Pérez, Carlos Andrés & Burbano, Carolina & Londoño, Harold, 2017. "Compás Empresarial y de Competitividad No. 1 Regiones para vivir Índice Sintético de Calidad de Vida Departamental [Business and Competitiveness Compass No. 1 Regions to live Synthetic index of dep," MPRA Paper 89759, University Library of Munich, Germany, revised Nov 2017.
    16. António Pedro Duarte Silva, 2002. "Discarding Variables in a Principal Component Analysis: Algorithms for All-Subsets Comparisons," Computational Statistics, Springer, vol. 17(2), pages 251-271, July.
    17. Sokbae Lee & Serena Ng, 2020. "An Econometric Perspective on Algorithmic Subsampling," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 45-80, August.
    18. Ali Polat & Mehmet Yesilyaprak & Raci Kaya, 2014. "Understanding Islamic Bank Selection of Customers: A Field Research from Turkish Participation Banks," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 5(4), pages 22-38, October.
    19. Michael Brusco & Renu Singh & Douglas Steinley, 2009. "Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 705-726, December.
    20. Doyo Enki & Nickolay Trendafilov, 2012. "Sparse principal components by semi-partition clustering," Computational Statistics, Springer, vol. 27(4), pages 605-626, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:9:y:2016:i:7:p:520-:d:73353. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.