Multivariable Analysis for Advanced Analytics of Wind Turbine Management

  • Alberto Pliego Marugán
  • Fausto Pedro García MárquezEmail author
  • Mayorkinos Papaelias
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 502)


Operation and maintenance tasks on the wind turbines have an essential role to ensure the correct condition of the system and to minimize losses and increase the productivity. The condition monitoring systems installed on the main components of the wind turbines provide information about the tasks that should be carried out over the time. A novel statistical methodology for multivariable analysis of big data from wind turbines is presented in this paper. The objective is to analyse the necessary information from the condition monitoring systems installed in wind farms. The novel approach filters the main parameters from the collected signals and uses advanced computational techniques for evaluating the data and giving meaning to them. The main advantage of the approach is the possibility of the big data analysis based on the main information available.


Condition monitoring systems Multivariable analysis Wind turbine maintenance Neural networks 



The work reported herewith has been financially supported by the Spanish Ministerio de Economíay Competitividad, under Research Grant DPI2015-67264, and the FP7 Research project with reference FP-7-Energy-2012-TREN-1:322430.


  1. 1.
    Arbib MA (2003) The handbook of brain theory and neural networks. MIT Press, CambridgeGoogle Scholar
  2. 2.
    Asht S, Dass R (2012) Pattern recognition techniques: a review. Int J Comput Sci Telecommun 3(8)Google Scholar
  3. 3.
    Azevedo HDM, Araújo AM, Bouchonneau N (2016) A review of wind turbine bearing condition monitoring: state of the art and challenges. Renew Sustain Energy Rev 56:368–379CrossRefGoogle Scholar
  4. 4.
    Chen D, Wang W (2002) Classification of wavelet map patterns using multi-layer neural networks for gear fault detection. Mech Syst Signal Process 16(4):695–704CrossRefGoogle Scholar
  5. 5.
    Chen Z, Guerrero JM, Blaabjerg F (2009) A review of the state of the art of power electronics for wind turbines. IEEE Trans Power Electron 24(8):1859–1875CrossRefGoogle Scholar
  6. 6.
    de la Hermosa González RR, Márquez FPG et al (2015) Maintenance management of wind turbines structures via mfcs and wavelet transforms. Renew Sustain Energy Rev 48:472–482CrossRefGoogle Scholar
  7. 7.
    Esteban MD, Diez JJ, López JS, Negro V (2011) Why offshore wind energy? Renew Energy 36(2):444–450CrossRefGoogle Scholar
  8. 8.
    Gomez Munoz C, la Hermosa De et al (2014) A novel approach to fault detection and diagnosis on wind turbines. Glob Nest J 16(6):1029–1037Google Scholar
  9. 9.
    Gómez Muñoz CQ, García Márquez FP (2016) A new fault location approach for acoustic emission techniques in wind turbines. Energies 9(1):40CrossRefGoogle Scholar
  10. 10.
    Guo P, Bai N (2011) Wind turbine gearbox condition monitoring with AAKR and moving window statistic methods. Energies 4(11):2077–2093CrossRefGoogle Scholar
  11. 11.
    Jiang W, Fan Q, Gong J (2010) Optimization of welding joint between tower and bottom flange based on residual stress considerations in a wind turbine. Energy 35(1):461–467CrossRefGoogle Scholar
  12. 12.
    Lloyd G (2007) Guideline for the certification of condition monitoring systems for wind turbines. Hamburg, GermanyGoogle Scholar
  13. 13.
    Márquez FPG, Muñoz JMC (2012) A pattern recognition and data analysis method for maintenance management. Int J Syst Sci 43(6):1014–1028CrossRefGoogle Scholar
  14. 14.
    Márquez FPG, Pérez JMP, Marugán AP, Papaelias M (2016) Identification of critical components of wind turbines using FTA over the time. Renew Energy 87:869–883CrossRefGoogle Scholar
  15. 15.
    Masters T (1993) Practical neural network recipes in C++. Morgan Kaufmann, San FranciscoGoogle Scholar
  16. 16.
    McMillan D, Ault GW (2007) Quantification of condition monitoring benefit for offshore wind turbines. Wind Eng 31(4):267–285CrossRefGoogle Scholar
  17. 17.
    Novaes Pires de  G, Alencar E, Kraj A (2010) Remote conditioning monitoring system for a hybrid wind diesel system-application at fernando de naronha island, brasilGoogle Scholar
  18. 18.
    OPTIMUS (2014) Demonstration of methods and tools for the optimisation of operational reliability of large-scale industrial wind turbines, optimus projectGoogle Scholar
  19. 19.
    Papaelias M, Cheng L et al (2016) Inspection and structural health monitoring techniques for concentrated solar power plants. Renew Energy 85:1178–1191CrossRefGoogle Scholar
  20. 20.
    Pliego Marugán A, García Márquez F (2014) System management for remote condition monitoring in railway systems. In: 6th IET conference on railway condition monitoring (RCM 2014), IET, pp 1–10Google Scholar
  21. 21.
    Pliego Marugán A, García Márquez FP, Pinar Pérez JM (2016) Optimal maintenance management of offshore wind farms. Energies 9(1):46CrossRefGoogle Scholar
  22. 22.
    Pullen A (2015) Global wind report annual market update 2014. Technical report, Global Wind Report Annual Market, pp 3–9Google Scholar
  23. 23.
    Tavner P (2012) Offshore wind turbine reliability, availability and maintenance. Institution of Engineering and Technology, LondonCrossRefGoogle Scholar
  24. 24.
    Tsai CS, Hsieh CT, Huang SJ (2006) Enhancement of damage-detection of wind turbine blades via cwt-based approaches. IEEE Trans Energy Convers 21(3):776–781CrossRefGoogle Scholar
  25. 25.
    Wymore ML, Van Dam JE et al (2015) A survey of health monitoring systems for wind turbines. Renew Sustain Energy Rev 52:976–990CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  • Alberto Pliego Marugán
    • 1
  • Fausto Pedro García Márquez
    • 1
    Email author
  • Mayorkinos Papaelias
    • 2
  1. 1.Ingenium Research GroupUniversidad Castilla-La ManchaCiudad RealSpain
  2. 2.University of BirminghamBirminghamUK

Personalised recommendations