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Models for monitoring wind farm power

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  • Kusiak, Andrew
  • Zheng, Haiyang
  • Song, Zhe

Abstract

Different models for monitoring wind farm power output are considered. Data mining and evolutionary computation are integrated for building the models for prediction and monitoring. Different models using wind speed as input to predict the total power output of a wind farm are compared and analyzed. The k-nearest neighbor model, combined with the principal component analysis approach, outperforms other models studied in this research. However, this model performs poorly when the conditions of the wind farm are abnormal. The latter implies that the original data contains many noisy points that need to be filtered. An evolutionary computation algorithm is used to build a nonlinear parametric model to monitor the wind farm performance. This model filters the outliers according to the residual approach and control charts. The k-nearest neighbor model produces good performance for the wind farm operating in normal conditions.

Suggested Citation

  • Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "Models for monitoring wind farm power," Renewable Energy, Elsevier, vol. 34(3), pages 583-590.
    • Handle: RePEc:eee:renene:v:34:y:2009:i:3:p:583-590
    DOI: 10.1016/j.renene.2008.05.032
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    References listed on IDEAS

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    1. Santoso, Surya & Le, Ha Thu, 2007. "Fundamental time–domain wind turbine models for wind power studies," Renewable Energy, Elsevier, vol. 32(14), pages 2436-2452.
    2. Hothorn, Torsten & Lausen, Berthold, 2005. "Bundling classifiers by bagging trees," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1068-1078, June.
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