Article Type:
Research Article

An Improved Method for Estimating the Wind Power Density Distribution Function

Mark L. Morrissey School of Meteorology, University of Oklahoma, Norman, Oklahoma

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Werner E. Cook School of Meteorology, University of Oklahoma, Norman, Oklahoma

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J. Scott Greene Department of Geography, University of Oklahoma, and Oklahoma Wind Power Initiative, Norman, Oklahoma

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Print Publication:
01 Jul 2010
DOI:
https://doi.org/10.1175/2010JTECHA1390.1
Page(s):
1153–1164
Restricted access

Abstract

The wind power density (WPD) distribution curve is essential for wind power assessment and wind turbine engineering. The usual practice of estimating this curve from wind speed data is to first estimate the wind speed probability density function (PDF) using a nonparametric or parametric method. The density function is then multiplied by one-half the wind speed cubed times the air density. Unfortunately, this means that minor errors in the estimation of the wind speed PDF can result in large errors in the WPD distribution curve because the cubic term in the WPD function magnifies the error. To avoid this problem, this paper presents a new method of estimating the WPD distribution curve through a direct estimation of the curve using a Gauss–Hermite expansion. It is demonstrated that the proposed method provides a much more reliable estimate of the WPD distribution curve.

Corresponding author address: Mark L. Morrissey, School of Meteorology, University of Oklahoma, 120 David L. Boren Blvd., Suite 5900, Norman, OK 73072. Email: mmorriss@ou.edu

Abstract

The wind power density (WPD) distribution curve is essential for wind power assessment and wind turbine engineering. The usual practice of estimating this curve from wind speed data is to first estimate the wind speed probability density function (PDF) using a nonparametric or parametric method. The density function is then multiplied by one-half the wind speed cubed times the air density. Unfortunately, this means that minor errors in the estimation of the wind speed PDF can result in large errors in the WPD distribution curve because the cubic term in the WPD function magnifies the error. To avoid this problem, this paper presents a new method of estimating the WPD distribution curve through a direct estimation of the curve using a Gauss–Hermite expansion. It is demonstrated that the proposed method provides a much more reliable estimate of the WPD distribution curve.

Corresponding author address: Mark L. Morrissey, School of Meteorology, University of Oklahoma, 120 David L. Boren Blvd., Suite 5900, Norman, OK 73072. Email: mmorriss@ou.edu

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