Applications of K-nearest neighbor algorithm in intelligent diagnosis of wind turbine blades damage

Yaochi Tang, Yunchi Chang and Kuohao Li

Renewable Energy, 2023, vol. 212, issue C, 855-864

Abstract: Heavy losses in the wind power generation system are incurred when the wind turbine blades are severely damaged beyond repair. Therefore, an immediate repair at an initial stage is an economical method. There are limitations and difficulties in diagnosing initial damage to wind turbine blades. For example, the measurement cannot be performed during actual operation, the diagnostic machine learning model calculations are too complex, and so on. This study measured the noise signals in the operation of wind turbines. The k-nearest neighbor (k-NN) of supervised learning was used as the diagnostic method. The calculation of generalized fractal dimensions (GFDs) was used as diagnostic feature selection. The result shows an accuracy of 98.9%. Compared to other algorithms, the k-NN algorithm is simple and easy to understand and implement. To reduce the amount of calculation of the machine learning model, the optimum numerical combination of three major parameters is found in this study. These major parameters are (1) scale index of GFDs, (2) number of neighbor points in the algorithm, and (3) range formula. According to the findings, the k-NN algorithm model can achieve high accuracy. The optimum numerical combination of three major parameters provides a rapid, convenient, and efficient diagnostic method.

Keywords: Supervised learning; K-nearest neighbor; Generalized fractal dimensions; Scale index (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)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148123007164
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:212:y:2023:i:c:p:855-864

DOI: 10.1016/j.renene.2023.05.087

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().