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Advanced Data Mining Techniques for Power Performance Verification of an On-Shore Wind Farm

  • Francesco Castellani
  • Alberto Garinei
  • Ludovico Terzi
  • Davide Astolfi
  • Michele Moretti
  • Andrea Lombardi
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

The monitoring of wind energy production is fundamental to improve the performances of a wind farm during the operational phase. In order to perform reliable operational analysis, data mining of all available information spreading out from turbine control systems is required. In this work a Supervisory Control and Data Acquisition (SCADA) data analysis was performed on a small wind farm and new post-processing methods are proposed for condition monitoring of the aerogenerators. Indicators are defined to detect the malfunctioning of a wind turbine and to select meaningful data to investigate the causes of the anomalous behaviour of a turbine. The operating state database is used to collect information about the proper power production of a wind turbine, becoming a tool that can be used to verify if the contractual obligations between the original equipment manufacturer and the wind farm operator are met. Results demonstrate that a proper selection of the SCADA data can be very useful to measure the real performances of a wind farm and thus to define optimal repair/replacement and preventive maintenance policies that play a major role in case of energy production.

Keywords

Wind energy Wind turbine condition monitoring SCADA analysis Data mining 

References

  1. 1.
    Catmull S (2011) Self-organising map based condition monitoring of wind turbines. In Proceedings of EWEA conference, 14–17 Mar 2011, Brussels, BelgiumGoogle Scholar
  2. 2.
    Lapira E, Brisset D, Davari Ardakani H, Siegel D, Lee J (2012) Wind turbine performance assessment using multi-regime modeling approach. Renewable Energy 45:86–95CrossRefGoogle Scholar
  3. 3.
    Kusiak A, Li W (2011) The prediction and diagnosis of wind turbine faults. Renewable Energy 36:16–23CrossRefGoogle Scholar
  4. 4.
    Carvalho H, Gaião M, Guedes R (2010) Wind farm power performance test, in the scope of the IEC 61400-12.3. In Proceedings of EWEC 2010 European wind energy conference and exhibition, 20–23 Apr 2010, Warsaw, PolandGoogle Scholar
  5. 5.
    Gill S, Stephen B, Galloway S (2012) Wind turbine condition assessment through power curve copula modeling. IEEE Trans Sustain Energy 3(1):94–101CrossRefGoogle Scholar
  6. 6.
    Kusiak A, Verna A (2012) Monitoring wind farms with performance curves. IEEE Transactions Sustain Energy 4(1):192–199CrossRefGoogle Scholar
  7. 7.
    Gallardo-Calles J-M, Colmenar-Santos A, Ontanon-Ruiz J, Castro-Gil M (2013) Wind control centres: state of the art. Renewable Energy 51:93–100CrossRefGoogle Scholar
  8. 8.
    Schlechtingen M, Ferreira Santos I, Achiche S (2013) Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: system description. Appl Soft Comput 13(1):259–270CrossRefGoogle Scholar
  9. 9.
    Castellani F, Vignaroli A (2012) An application of the actuator disc model for wind turbine wakes calculations. Appl Energy 101:432. ISSN 0306-2619, doi: 10.1016/j.apenergy.2012.04.039 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Francesco Castellani
    • 1
  • Alberto Garinei
    • 2
  • Ludovico Terzi
    • 3
  • Davide Astolfi
    • 1
  • Michele Moretti
    • 1
  • Andrea Lombardi
    • 3
  1. 1.Department of Industrial EngineeringUniversity of PerugiaPerugiaItaly
  2. 2.DMIIUniversità degli Studi Guglielmo MarconiRomeItaly
  3. 3.Sorgenia Green srlMilanItaly

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