A production economics analysis for quantifying the efficiency of wind turbines Academic Article uri icon


  • Copyright © 2017 John Wiley & Sons, Ltd. We quantify the productive efficiency of a wind turbine, using power output and environmental variable data, measured either at the turbine or at a meteorological mast near the turbine. The methods described can potentially help with decision makings in asset procurement, maintenance planning, or wind turbine control optimization. The current recommendation from the International Electrotechnical Commission regarding turbine performance evaluation is to use a power curve or power coefficient. What is commonly used in practice is the average performance power curve or power coefficient. When using the power curve to quantify productive efficiency, one crucial shortcoming is the lack of a common best performance benchmark, while the power coefficient approach uses an absolute efficiency measure that is not achievable. We introduce a new approach for efficiency quantification based upon production economics' concepts which provides estimates of a best performance benchmark. Our specific approach has two main components: (a) a best performance power curve is estimated and used together with the average performance curve to show how well a turbine has performed relative to its full potential; and (b) a covariate matching procedure is developed to control for environmental influences for the comparison of turbine performances over different periods. Through a simulation study, we demonstrate that the proposed efficiency is more sensitive to potential changes in the turbine. When analyzing multi-year wind turbine data, we observe that the turbine's efficiency is improving during the first 2 years of operation and then remains relatively constant during years 3 and 4. Copyright © 2017 John Wiley & Sons, Ltd.

published proceedings


author list (cited authors)

  • Hwangbo, H., Johnson, A., & Ding, Y. u.

citation count

  • 13

complete list of authors

  • Hwangbo, Hoon||Johnson, Andrew||Ding, Yu

publication date

  • September 2017