Wind power prediction using enhanced parametric wind power curve modeling

Renani, Ehsan Taslimi; Elias, Mohamad Fathi Mohamad; Rahim, N. Abd

doi:10.1049/cp.2016.1359

Cited by 1 publication

(1 citation statement)

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“…Accurate identification of wind turbine power curve plays a fundamental role in different applications, such as wind power assessment and forecasting [1,2], wind turbine selection, condition monitoring [3][4][5], troubleshooting, and predictive control and optimisation [6,7]. In practice, the theoretical power captured by a wind turbine depends, among other things, on the air density, which is itself a function of temperature, pressure and humidity, which clearly depend on where the turbine is installed [1].…”

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Wind turbine power curve estimation based on earth mover distance and artificial neural networks

Bai

Crisostomi

Raugi

et al. 2019

IET Renewable Power Generation

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A data-based estimation of the wind-power curve in wind turbines may be a challenging task due to the presence of anomalous data, possibly due to wrong sensor reads, operation halts, malfunctions or other. In this study, the authors describe a data-based procedure to build a robust and accurate estimate of the wind-power curve. In particular, they combine a joint clustering procedure, where both the wind speeds and the power data are clustered, with an Earth Mover Distance-based Extreme Learning Machine algorithm to filter out data that poorly contribute to explain the unknown curve. After estimating the cut-in and the rated speed, they use a radial basis function neural network to fit the filtered data and obtain the curve estimate. They extensively compared the proposed procedure against other conventional methodologies over measured data of nine turbines, to assess and discuss its performance.

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