A multivariable wind turbine power curve modeling method considering segment control differences and short-time self-dependence

Yanhui Qiao, Shuang Han, Yajie Zhang, Yongqian Liu and Jie Yan

Renewable Energy, 2024, vol. 222, issue C

Abstract: Multivariable wind turbine power curve model is crucial for the performance degradation evaluation of wind turbine. However, the existing models have low modeling accuracy without considering the wind energy capture difference caused by segment control, and the inputs cannot represent the actual wind energy resources owing to the neglect of the short-term self-dependence of environmental parameters, which makes it difficult to ensure the performance degradation evaluation accuracy of wind turbine. To address these problems, a multivariable wind turbine power curve modeling method considering segment control differences and short-time self-dependence is proposed. First, an abnormal data cleaning method based on timing matching and bidirectional quartile algorithm is proposed to clean the abnormal data. Then, the multivariate wind turbine power curve model based on piece-wise regression of multiple environmental parameters is constructed, and applied to the performance degradation evaluation of wind turbine. The results demonstrated that the proposed abnormal data cleaning method can effectively solve the problem that the data categories of transition region between abnormal and normal data cannot be identified. Meanwhile, the proposed multivariable wind turbine power curve model can effectively improve the modelling accuracy and ensure the performance degradation evaluation accuracy of wind turbine under different wind energy resources.

Keywords: Wind turbine; Multivariable power curve model; Segment control differences; Short-time self-dependence; Timing matching algorithm (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:222:y:2024:i:c:s0960148123018098

DOI: 10.1016/j.renene.2023.119894

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