Blade icing detection of wind turbine based on multi-feature and multi-classifier fusion. Issue 4 (August 2022)

• Record Type: Journal Article
• Authors: Lu, Chao; He, Guodong; Shou, Chunhui; Wu, Yiwen; Shen, Yang; Zhu, Jinkui
• Is Part Of: Wind engineering. Volume 46:Issue 4(2022)
• Page Start: 1236
• Page End: 1246
• Publication Date: 2022-08
• Subjects: Wind turbine blades -- blade icing detection -- data-driven -- multi-feature -- multi-classifier fusion; Wind-pressure -- Periodicals; Winds -- Periodicals; Wind power -- Periodicals; Engineering meteorology -- Periodicals; Pression du vent; Vents; Énergie éolienne; Météorologie appliquée; Engineering meteorology; Wind power; Wind-pressure; Winds; Periodicals; 621.4505
• DOI: 10.1177/0309524X221075590
• Languages: English
• ISSNs: 0309-524X

Abstract

Wind farms are usually located in high altitude areas with a high probability of ice occurrence. Blade icing has the potential to result in unexpected mechanical failures and downtimes. In order to avoid these problems, the priority we need to do is to detect blade icing accurately. For this purpose, a novel icing detection method based on multi-feature and multi-classifier fusion is proposed in this paper. Firstly, multi-feature composed of basic features and statistical features are extracted from the operational data. Significant features are then extracted by utilizing Light Gradient Boosting Machine. Secondly, a multi-classifier fusion approach is employed to build an fusion model, which aims to obtain a much more accurate estimation for the icing state. Overall, the proposed method in this paper can achieve more accurate detection on blade icing, compared with other models. This will minimize false alarms, helping wind farms manage the operations more efficiently.