Abnormal data cleaning for wind turbines by image segmentation based on active shape model and class uncertainty

Guoyuan Liang, Yahao Su, Xinyu Wu, Jiajun Ma, Huan Long and Zhe Song

Renewable Energy, 2023, vol. 216, issue C

Abstract: Wind power curve describes the relationship between wind speed and output power of wind turbine. In this paper, we propose a novel image-based abnormal data cleaning algorithm aiming to provide high quality data for wind power curve modeling and relevant applications. For image binarization, a multiscale space is built to determine the optimal scale at which the most stable as well as informative shape representations are extracted. The active shape model is utilized to model the power curve shape and generate a set of segmentation candidates for filtering abnormal data. An energy space is constructed based on class uncertainty map generated from dual feature images and eventually the optimal segmentation boundary is determined by an optimization process. Comparative experiments are conducted over the real-world datasets consisted of 37 wind turbines from two wind farms located in West and East China respectively. The accuracy reaches as high as 0.95, the precision rate P is 0.98, recall rate R is 0.99 and F1-score is 0.96, which demonstrates the superior performance of the proposed method. Besides, the reproducibility and sensitivity analyses are also carried out to assess the algorithm’s performance.

Keywords: Class uncertainty; Wind power curve; Data cleaning; Active shape model; Binary distance transform; Image segmentation (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:216:y:2023:i:c:s0960148123008716

DOI: 10.1016/j.renene.2023.118965

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