Development of an enhanced parametric model for wind turbine power curve

Ehsan Taslimi-Renani, Mostafa Modiri-Delshad, Mohamad Fathi Mohamad Elias and Nasrudin Abd. Rahim

Applied Energy, 2016, vol. 177, issue C, 544-552

Abstract: Modeling of wind turbine power curve is greatly important in performance monitoring of the turbine and also in forecasting the wind power generation. In this paper, an accurate parametric model called modified hyperbolic tangent (MHTan) is proposed to characterize power curve of the wind turbine. The paper also presents the development of both parametric and nonparametric models of wind turbine power curve. In addition, least square error (LSE) and maximum likelihood estimation (MLE) are employed to estimate vector parameter of the proposed model. Here, three evolutionary algorithms, namely, particle swarm optimization, Cuckoo search, and backtracking search algorithm aid LSE and MLE. The performance of all presented methods is evaluated by a real data collected from a wind farm in Iran as well as three statistically generated data sets. The results demonstrate the efficiency of the proposed model compared to some other existing parametric and nonparametric models.

Keywords: Adaptive neuro fuzzy inference system; Data mining; Metaheuristic algorithm; Parametric models; Power curve; Wind turbine (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (28)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:177:y:2016:i:c:p:544-552

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DOI: 10.1016/j.apenergy.2016.05.124

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