Analyzing State Dynamics of Wind Turbines Through SCADA Data Mining

Conference paper
Part of the Applied Condition Monitoring book series (ACM, volume 4)


Supervisory Control And Data Acquisition (SCADA) control systems have become ubiquitous in modern wind energy technology. Exploiting their potentialities is a keystone for performance optimization and to improve the operational control feeding smart electric grids. Yet, tackling the complexity of SCADA data sets is a challenging task. The philosophy underlying the present work is discretization of the continuous motion of machine states and error signals: doing this, one ends up with simplified databases, acting on which with statistical methods provide powerful insight. Indicators on the quality of turbine functionality and on the nature of error signals are formulated, and the distribution of errors as a function of wind intensity is studied. The methods are tested on the data of a wind farm in southern Italy, and it is shown that they are indeed capable of assessing performances and interpreting the nature of occurred errors.


Wind energy Wind turbines SCADA control system Performance evaluation 


  1. 1.
    Wind Power Monthly (2013) Condition Monitoring. Expert report editionGoogle Scholar
  2. 2.
    Wind Power Monthly (2014) Wind Turbine Control Systems. Expert report editionGoogle Scholar
  3. 3.
    Kusiak A, Verma A (2012) Analyzing bearing faults in wind turbines: a data-mining approach. Renew Energy 48:110–116CrossRefGoogle Scholar
  4. 4.
    Zhang ZY, Wang KS (2014) Wind turbine fault detection based on SCADA data analysis using ann. Advan Manuf 2(1):70–78CrossRefGoogle Scholar
  5. 5.
    Papatheou E, Dervilis N, Maguire E, Worden K (2014) Wind turbine structural health monitoring: a short investigation based on SCADA data. In: Le Cam V, Mevel L, Schoefs F (eds) EWSHM—7th European workshop on structural health monitoring, Nantes. IFFSTTAR, Inria, Université de NantesGoogle Scholar
  6. 6.
    Yan Y, Li J, Sun P, Zhang X (2014) Study on parameters modeling of wind turbines using SCADA data. Sens Transducers 176(8):237–243Google Scholar
  7. 7.
    Kusiak A, Zheng H (2010) Optimization of wind turbine energy and power factor with an evolutionary computation algorithm. Energy 35(3):1324–1332CrossRefGoogle Scholar
  8. 8.
    Barthelmie RJ, Hansen KS, Pryor SC (2013) Meteorological controls on wind turbine wakes. Proc IEEE 101(4):1010–1019CrossRefGoogle Scholar
  9. 9.
    Barthelmie RJ, Pryor SC, Frandsen ST, Hansen KS, Schepers JG, Rados K, Schlez W, Neubert A, Jensen LE, Neckelmann S (2010) Quantifying the impact of wind turbine wakes on power output at offshore wind farms. J Atmos Oceanic Technol 27(8):1302–1317CrossRefGoogle Scholar
  10. 10.
    Hansen KS, Barthelmie RJ, Jensen LE, Sommer A (2012) The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at horns rev wind farm. Wind Energy 15(1):183–196CrossRefGoogle Scholar
  11. 11.
    Castellani F, Garinei A, Terzi L, Astolfi D, Gaudiosi M (2014) Improving windfarm operation practice through numerical modelling and supervisory control and data acquisition data analysis. Renew Power Gener IET 8(4):367–379CrossRefGoogle Scholar
  12. 12.
    Astolfi D, Castellani F, Terzi L (2014) Fault prevention and diagnosis through scada temperature data analysis of an onshore wind farm. Diagnostyka 15(2)Google Scholar
  13. 13.
    Castellani F, Astolfi D, Terzi L, Hansen KS, Rodrigo JS (2014) Analysing wind farm efficiency on complex terrains. J Phys Conf Ser 524:012142 IOP PublishingCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of EngineeringUniversity of PerugiaPerugiaItaly
  2. 2.Renvico srlMilanoItaly

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