On-line monitoring of power curves

Andrew Kusiak, Haiyang Zheng and Zhe Song

Renewable Energy, 2009, vol. 34, issue 6, 1487-1493

Abstract: A data-driven approach to the performance analysis of wind turbines is presented. Turbine performance is captured with a power curve. The power curves are constructed using historical wind turbine data. Three power curve models are developed, one by the least squares method and the other by the maximum likelihood estimation method. The models are solved by an evolutionary strategy algorithm. The power curve model constructed by the least squares method outperforms the one built by the maximum likelihood approach. The third model is non-parametric and is built with the k-nearest neighbor (k-NN) algorithm. The least squares (parametric) model and the non-parametric model are used for on-line monitoring of the power curve and their performance is analyzed.

Keywords: Power curve; Turbine monitoring; Data mining; Evolutionary computation; Least squares method; Maximium likelihood estimation (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (52)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:34:y:2009:i:6:p:1487-1493

DOI: 10.1016/j.renene.2008.10.022

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