Copula-based model for wind turbine power curve outlier rejection

Authors

  • Yue Wang,

    Corresponding author
    1. Institute for Energy and Environment, Electrical and Electronic Engineering, University of Strathclyde, Glasgow, UK
    • Correspondence: Y. Wang, Institute for Energy and Environment, Electrical and Electronic Engineering, University of Strathclyde, 204 George Street, Glasgow, G1 1XW, UK.

      E-mail: y.wang@strath.ac.uk

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  • David G. Infield,

    1. Institute for Energy and Environment, Electrical and Electronic Engineering, University of Strathclyde, Glasgow, UK
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  • Bruce Stephen,

    1. Institute for Energy and Environment, Electrical and Electronic Engineering, University of Strathclyde, Glasgow, UK
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  • Stuart J. Galloway

    1. Institute for Energy and Environment, Electrical and Electronic Engineering, University of Strathclyde, Glasgow, UK
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ABSTRACT

Power curve measurements provide a conventional and effective means of assessing the performance of a wind turbine, both commercially and technically. Increasingly high wind penetration in power systems and offshore accessibility issues make it even more important to monitor the condition and performance of wind turbines based on timely and accurate wind speed and power measurements. Power curve data from Supervisory Control and Data Acquisition (SCADA) system records, however, often contain significant measurement deviations, which are commonly produced as a consequence of wind turbine operational transitions rather than stemming from physical degradation of the plant. Using such raw data for wind turbine condition monitoring purposes is thus likely to lead to high false alarm rates, which would make the actual fault detection unreliable and would potentially add unnecessarily to the costs of maintenance. To this end, this paper proposes a probabilistic method for excluding outliers, developed around a copula-based joint probability model. This approach has the capability of capturing the complex non-linear multivariate relationship between parameters, based on their univariate marginal distributions; through the use of a copula, data points that deviate significantly from the consolidated power curve can then be removed depending on this derived joint probability distribution. After filtering the data in this manner, it is shown how the resulting power curves are better defined and less subject to uncertainty, whilst broadly retaining the dominant statistical characteristics. These improved power curves make subsequent condition monitoring more effective in the reliable detection of faults. Copyright © 2013 John Wiley & Sons, Ltd.

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