Yaw-adjusted wind power curve modeling: A local regression approach

Praanjal Nasery and Ahmed Aziz Ezzat

Renewable Energy, 2023, vol. 202, issue C, 1368-1376

Abstract: Accurate estimation of wind power curves using field data is instrumental to several wind farm operations including productivity assessment, power output estimation, operations and maintenance, among others. Existing methods for estimating wind power curves mainly rely on environmental variables (e.g., wind speed, direction, density) as inputs to construct the wind-to-power relationship. This paper attempts to integrate yaw misalignment as an additional input to power curve models, constructing what is referred to hereinafter as “yaw-adjusted wind power curves.” Our analysis shows that integrating yaw misalignment into power curves is non-trivial, largely due to the overwhelming impact of environmental variables (mainly wind speed) on a turbine’s power output, which obscures the secondary effect of yaw errors on power production. In response, we propose a local-regression-based method which reconstructs the yaw-to-power relationship conditional on an effective neighborhood of environmental variables. Tested on operational data from two onshore wind turbines in France, our proposed approach achieves significant improvements, in terms of power estimation accuracy, relative to a set of prevalent statistical- and machine-learning-based power curve models.

Keywords: Local regression; Power curve; Wind energy; Yaw error (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:202:y:2023:i:c:p:1368-1376

DOI: 10.1016/j.renene.2022.12.001

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