Comparative study of binning and gaussian process based rotor curves of a wind turbine for the purpose of condition monitoring

Ravi Kumar Pandit, David Infield

Research output: Contribution to conferenceProceeding

11 Downloads (Pure)

Abstract

The wind turbines deteriorating performance cause failures, including catastrophic failures that lead to high operation and maintenance (O&M) cost. SCADA based continuous monitoring of wind turbines is a cost-effective approach and plays an essential role as turbine sizes increase, and they are placed in more remote locations, for example, offshore to improve the performance of wind turbines and reduces the O&M cost. Various studies suggest that the internal operation of the wind turbine depends on various variables, especially on rotor speed. The proper analysis of rotor speed can be useful for constructing effective SCADA based models for wind turbine condition monitoring. Gaussian Process (GP) is a nonparametric, stochastic process that's designed to solve regression and probabilistic classification problems. GP models powerful to solve nonlinear systems, however, its application are not much explored in wind turbine condition monitoring.This paper describes a wind turbine condition monitoring Gaussian Process technique that uses the rotor speed to derive a rotor curve for wind turbine condition monitoring. Developed GP model, then compared with the conventional approach based on binned rotor curves together with individual bin probability distributions to identify operational anomalies. The proposed techniques have been validated experimentally using SCADA data sets obtained from operational turbines. Finally, comparative analysis of these techniques described outlining the strength and weakness of individual models.
Original languageEnglish
Number of pages7
Publication statusPublished - 30 Aug 2018
Event3rd International Conference on Offshore Renewable Energy - Glasgow, United Kingdom
Duration: 29 Aug 201830 Aug 2018
Conference number: 3
http://www.offshore-renewables.co.uk/

Conference

Conference3rd International Conference on Offshore Renewable Energy
Abbreviated title CORE 2018
CountryUnited Kingdom
CityGlasgow
Period29/08/1830/08/18
Internet address

Keywords

  • wind turbine
  • SCADA analysis
  • gaussian process
  • rotor speed
  • condition monitoring
  • Prediction

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    • 7 Article
    • 3 Conference contribution book
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  • 2 Citations (Scopus)
    13 Downloads (Pure)

    Comparative analysis of binning and support vector regression for wind turbine rotor speed based power curve use in condition monitoring

    Pandit, R. & Infield, D., 13 Dec 2018, 2018 53rd International Universities Power Engineering Conference (UPEC). Piscataway, NJ: IEEE, 6 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution book

    Open Access
    File
  • 3 Citations (Scopus)
    14 Downloads (Pure)
    Open Access
    File
  • 7 Citations (Scopus)
    10 Downloads (Pure)

    Cite this

    Pandit, R. K., & Infield, D. (2018). Comparative study of binning and gaussian process based rotor curves of a wind turbine for the purpose of condition monitoring. 3rd International Conference on Offshore Renewable Energy, Glasgow, United Kingdom.