SCADA based nonparametric models for condition monitoring of a wind turbine

Ravi Kumar Pandit, David Infield

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Abstract

High operation and maintenance costs for offshore wind turbines push up the LCOE of offshore wind energy. Unscheduled maintenance due to unanticipated failures is the most prominent driver of the maintenance cost which reinforces the drive towards condition-based maintenance. SCADA based condition monitoring is a cost-effective approach where power curve used to assess the performance of a wind turbine. Such power curves are useful in identification of wind turbine abnormal behaviour. IEC standard 61400-12-1 outlines the guidelines for power curve modelling based on binning. However, establishing such a power curve takes considerable time and is far too slow to reflect changes in performance to be used directly for condition monitoring. To address this, data-driven, nonparametric models being used instead. Gaussian Process models and regression trees are commonly used nonlinear, nonparametric models useful in forecasting and prediction applications. In this paper, two nonparametric methods are proposed for power curve modelling. The Gaussian Process treated as the benchmark model, and a comparative analysis was undertaken using a Regression tree model; the advantages and limitations of each model will be outlined. The performance of these regression models is validated using readily available SCADA datasets from a healthy wind turbine operating under normal conditions.
Original languageEnglish
Pages (from-to)1-5
Number of pages5
JournalThe Journal of Engineering
DOIs
Publication statusPublished - 15 Mar 2019
EventThe 7th International Conference on Renewable Power Generation (RPG2018) - DTU, Lyngby, Copenhagen, Denmark
Duration: 26 Sep 201827 Sep 2018
https://events.theiet.org/rpg/index.cfm

Keywords

  • condition monitoring
  • power curves
  • wind turbines
  • SCADA data
  • Gaussian process models
  • decision trees

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  • 6 Citations (Scopus)
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    Comparison of advanced non-parametric models for wind turbine power curves

    Pandit, R. K., Infield, D. & Kolios, A., 8 Jul 2019, In : IET Renewable Power Generation. 13, 9, p. 1503-1510 8 p.

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    Incorporating air density into a Gaussian process wind turbine power curve model for improving fitting accuracy

    Pandit, R. K., Infield, D. & Carroll, J., 22 Oct 2018, In : Wind Energy. p. 1-14 14 p.

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