LeeDingGentonXie_2014SEP02.pdf - Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms Item Type Article

LeeDingGentonXie_2014SEP02.pdf - Power Curve Estimation...

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Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms Item Type Article Authors Lee, Giwhyun; Ding, Yu; Genton, Marc G.; Xie, Le Citation Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms 2015, 110 (509):56 Journal of the American Statistical Association Eprint version Post-print DOI 10.1080/01621459.2014.977385 Publisher Informa UK Limited Journal Journal of the American Statistical Association Rights This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on Apr. 22, 2015, available online: http:// wwww.tandfonline.com/10.1080/01621459.2014.977385. Download date 17/10/2019 09:58:19 Link to Item
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Power Curve Estimation with Multivariate Environmental Factors for Inland and Offshore Wind Farms Giwhyun Lee Korea Army Academy Yeongcheon, Republic of Korea email: [email protected] Yu Ding Industrial and Systems Engineering, Texas A&M University, TX, USA email: [email protected] Marc G. Genton CEMSE Division, King Abdullah University of Science and Technology, Saudi Arabia email: [email protected] Le Xie Electrical and Computer Engineering, Texas A&M University, TX, USA email: [email protected] 1
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Abstract In the wind industry, a power curve refers to the functional relationship between the power output generated by a wind turbine and the wind speed at the time of power generation. Power curves are used in practice for a number of important tasks including predicting wind power production and assessing a turbine’s energy production efficiency. Nevertheless, actual wind power data indicate that the power output is affected by more than just wind speed. Several other environmental factors, such as wind direction, air density, humidity, turbulence intensity, and wind shears, have potential impact. Yet, in industry practice, as well as in the literature, current power curve models primarily consider wind speed and, sometimes, wind speed and direction. We propose an additive multivariate kernel method that can include the aforementioned environmental factors as a new power curve model. Our model provides, conditional on a given environmental condition, both the point estimation and density estimation of power output. It is able to capture the nonlinear relationships between environmental factors and the wind power output, as well as the high-order interaction effects among some of the environmental factors. Using operational data associated with four turbines in an inland wind farm and two turbines in an offshore wind farm, we demonstrate the improvement achieved by our kernel method. Keywords : Additive multivariate kernel regression, Nonparametric estimation, Turbine performance assessment, Wind power forecast 2
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1. INTRODUCTION Wind energy is one of the fastest growing renewable energy sources. According to a report issued by the American Wind Energy Association (AWEA), wind power installation in the U.S. increased by more than a factor of ten in the past decade, from 4,232 megawatts (MW) in 2001 to 46,919 MW by the end of 2011 (AWEA 2012). The U.S. Department of Energy
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