Supervisory Control and Data Acquisition Analysis for Wind Turbine Maintenance Management

  • Isaac Segovia Ramirez
  • Fausto Pedro Garcia MarquezEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1190)


Wind energy is growing to become a competitive energy source. An efficient wind turbine maintenance management is required for ensuring the reliability of the energy production and the costs reduction. Supervisory control and data acquisition system provide information about the condition of the wind turbine by signals of the different subsystems and alarm activations in case of failure or malfunction. Due to the volume and variety of the data, operators require advanced analytics to control the performance of the wind turbines and the identification and prediction of failures. The novelty proposed in this work is based on statistical analysis for analyzing supervisory control and data acquisition data to optimize the use of the data in neural networks. The first phase is the alarm analysis, quantifying the critical alarms regarding on the number and time of activation. A filtering algorithm is developed for considering only interest periods with enough range to make the study. The second phase is based on the initial data treatment, classifying alarms and signals identifying the interest time periods. Neural network is defined and trained for evaluating the signal trends, with the aim of detecting the alarm activations cause. This information will be used in the maintenance management plan for programming maintenance tasks.


Wind turbine Maintenance management SCADA Neural network Wind turbine management 



The work reported herewith has been financially by the Dirección General de Universidades, Investigación e Innovación of Castilla-La Mancha, under Research Grant ProSeaWind project (Ref.: SBPLY/19/180501/000102).


  1. 1.
    Arcos Jiménez, A., Gómez Muñoz, C.Q., García Márquez, F.P.: Machine learning for wind turbine blades maintenance management. Energies 11(1), 13 (2018)CrossRefGoogle Scholar
  2. 2.
    Bose, B.K.: Neural network applications in power electronics and motor drives–an introduction and perspective. IEEE Trans. Ind. Electron. 54(1), 14–33 (2007)CrossRefGoogle Scholar
  3. 3.
    García Márquez, F.P., García-Pardo, I.P.: Principal component analysis applied to filtered signals for maintenance management. Qual. Reliab. Eng. Int. 26(6), 523–527 (2010)CrossRefGoogle Scholar
  4. 4.
    García Márquez, F.P., Pliego Marugán, A., et al.: Optimal dynamic analysis of electrical/electronic components in wind turbines. Energies 10(8), 1111 (2017)CrossRefGoogle Scholar
  5. 5.
    Gómez, C., García, F., et al.: A heuristic method for detecting and locating faults employing electromagnetic acoustic transducers. Eksploatacja i Niezawodność 19 (2017)Google Scholar
  6. 6.
    de la Hermosa Gonzalez, R.R., Márquez, F.P.G., et al.: Pattern recognition by wavelet transforms using macro fibre composites transducers. Mech. Syst. Sig. Process. 48(1–2), 339–350 (2014)Google Scholar
  7. 7.
    de la Hermosa González, R.R., Márquez, F.P.G., et al.: Maintenance management of wind turbines structures via MFCs and wavelet transforms. Renew. Sustain. Energy Rev. 48, 472–482 (2015)CrossRefGoogle Scholar
  8. 8.
    Herraiz, Á.H., Marugán, A.P., Márquez, F.P.G.: Optimal productivity in solar power plants based on machine learning and engineering management. In: International Conference on Management Science and Engineering Management, pp. 983–994. Springer, Heidelberg (2018)Google Scholar
  9. 9.
    Irena, I.: Renewable energy technologies: cost analysis series. Concentrating Solar Power (2012)Google Scholar
  10. 10.
    JantaraJunior, V., Basoalto, H., et al.: Evaluating the challenges associated with the long-term reliable operation of industrial wind turbine gearboxes. In: IOP Conference Series: Materials Science and Engineering, vol. 454, p. 012094. IOP Publishing (2018)Google Scholar
  11. 11.
    Jiménez, A.A., Muñoz, C.Q.G., et al.: Artificial intelligence for concentrated solar plant maintenance management. In: Proceedings of the Tenth International Conference on Management Science and Engineering Management, pp. 125–134. Springer, Heidelberg (2017)Google Scholar
  12. 12.
    Jiménez, A.A., Muñoz, C.Q.G., Márquez, F.P.G.: Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers. Reliab. Eng. Syst. Saf. 184, 2–12 (2019)CrossRefGoogle Scholar
  13. 13.
    Marquez, F.G.: An approach to remote condition monitoring systems management (2006)Google Scholar
  14. 14.
    Marquez, F.G., Singh, V., Papaelias, M.: A review of wind turbine maintenance management procedures. In: The Eighth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, pp. 1–14 (2011)Google Scholar
  15. 15.
    Márquez, F.G., Papaelias, J., Hermosa, R.R.: Wind turbines maintenance management based on FTA and BDD. In: International Conference on Renewable Energies and Power Quality (ICREPQ 2012), pp. 4–6 (2012)Google Scholar
  16. 16.
    Márquez, F.P.G.: A new method for maintenance management employing principal component analysis. Struct. Durability Health Monit. 6(2), 89–99 (2010)Google Scholar
  17. 17.
    Márquez, F.P.G., Muñoz, J.M.C.: A pattern recognition and data analysis method for maintenance management. Int. J. Syst. Sci. 43(6), 1014–1028 (2012)CrossRefGoogle Scholar
  18. 18.
    Márquez, F.P.G., Pérez, J.M.P., et al.: Identification of critical components of wind turbines using FTA over the time. Renew. Energy 87, 869–883 (2016)CrossRefGoogle Scholar
  19. 19.
    Marugán, A.P., Márquez, F.P.G.: SCADA and artificial neural networks for maintenance management. In: International Conference on Management Science and Engineering Management, pp. 912–919. Springer, Heidelberg (2017)Google Scholar
  20. 20.
    Marugán, A.P., Márquez, F.P.G., Papaelias, M.: Multivariable analysis for advanced analytics of wind turbine management. In: Proceedings of the Tenth International Conference on Management Science and Engineering Management, pp. 319–328. Springer, Heidelberg (2017)Google Scholar
  21. 21.
    Marugán, A.P., Márquez, F.P.G., et al.: A survey of artificial neural network in wind energy systems. Appl. Energy 228, 1822–1836 (2018)CrossRefGoogle Scholar
  22. 22.
    Marugán, A.P., Chacón, A.M.P., Márquez, F.P.G.: Reliability analysis of detecting false alarms that employ neural networks: a real case study on wind turbines. Reliab. Eng. Syst. Saf. 191(106), 574 (2019)Google Scholar
  23. 23.
    Mohammedi, K., Benmessaoud, T., et al.: Fuzzy logic applied to SCADA systems (2017)Google Scholar
  24. 24.
    Muñoz, C.Q.G., Márquez, F.P.G.: Future maintenance management in renewable energies. In: Renewable Energies, pp. 149–159. Springer, Heidelberg (2018)Google Scholar
  25. 25.
    Muñoz, C.Q.G., Jiménez, A.A., Márquez, F.P.G.: Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis. Renew. Energy 116, 42–54 (2018)CrossRefGoogle Scholar
  26. 26.
    Ohlenforst, K., Council, G.W.E.: Global wind report 2019 (2019)Google Scholar
  27. 27.
    Pedregal, D.J., García, F.P., Roberts, C.: An algorithmic approach for maintenance management based on advanced state space systems and harmonic regressions. Ann. Oper. Res. 166(1), 109–124 (2009)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Pérez, J.M.P., Márquez, F.P.G., Hernández, D.R.: Economic viability analysis for icing blades detection in wind turbines. J. Clean. Prod. 135, 1150–1160 (2016)CrossRefGoogle Scholar
  29. 29.
    Pliego Marugán, A., García Márquez, F.P.: Advanced analytics for detection and diagnosis of false alarms and faults: a real case study. Wind Energy 22(11), 1622–1635 (2019)CrossRefGoogle Scholar
  30. 30.
    Pliego Marugán, A., García Márquez, F.P., Lorente, J.: Decision making process via binary decision diagram. Int. J. Manag. Sci. Eng. Manag. 10(1), 3–8 (2015)Google Scholar
  31. 31.
    Pliego Marugán, A., García Márquez, F.P., Lev, B.: Optimal decision-making via binary decision diagrams for investments under a risky environment. Int. J. Prod. Res. 55(18), 5271–5286 (2017)CrossRefGoogle Scholar
  32. 32.
    Polinder, H., Ferreira, J.A., et al.: Trends in wind turbine generator systems. IEEE J. Emerg. Sel. Topics Power Electron. 1(3), 174–185 (2013)CrossRefGoogle Scholar
  33. 33.
    Tchakoua, P., Wamkeue, R., et al.: Wind turbine condition monitoring: state-of-the-art review, new trends, and future challenges. Energies 7(4), 2595–2630 (2014)CrossRefGoogle Scholar
  34. 34.
    Walford, C.A.: Wind turbine reliability: understanding and minimizing wind turbine operation and maintenance costs. Technical report, Sandia National Laboratories (2006)Google Scholar
  35. 35.
    Zhang, Z.Y., Wang, K.S.: Wind turbine fault detection based on SCADA data analysis using ANN. Adv. Manuf. 2(1), 70–78 (2014)CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Isaac Segovia Ramirez
    • 1
  • Fausto Pedro Garcia Marquez
    • 1
    Email author
  1. 1.Ingenium Research GroupUniversidad Castilla-La ManchaCiudad RealSpain

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