Support vector regression-based fatigue damage assessment method for wind turbine nacelle chassis. (October 2021)

• Record Type: Journal Article
• Authors: Liu, Yongqian; Tao, Tao; Zhao, Xingyu; Zhang, Ce; Ma, Yuanchi
• Is Part Of: Structures. Volume 33(2021)
• Page Start: 759
• Page End: 768
• Publication Date: 2021-10
• Subjects: Wind turbine -- Machine learning -- Nacelle chassis -- Fatigue damage assessment -- Equivalent stress amplitude; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2021.04.093
• Languages: English
• ISSNs: 2352-0124

Abstract

Highlights: Equivalent stress amplitude (ESA) defined as simple fatigue damage indicator. Pre-calculated ESA database established for different wind flow conditions. Database includes wind turbine nacelle chassis ESAs in varied wind conditions. The non-linear mapping relationship between wind condition and ESA is established. The proposed SVR-FDA method can achieve both fast and accurate assessment. Abstract: Fatigue damage assessment is critical for the structural design of the wind turbine nacelle chassis. However, existing fatigue damage indicators and assessment method cannot be both fast and accurate. In this paper, we propose a quantitative support vector regression-based fatigue damage assessment (SVR-FDA) method. First, the equivalent stress amplitude (ESA) is defined to simplify the fatigue damage indicator. Second, we establish the pre-calculated ESA database, including the ESAs of the wind turbine nacelle chassis under many varying wind flow conditions. Finally, based on the pre-calculated ESA database, we establish the SVR-FDA model, which can calculate the ESA of any given wind flow condition. A wind turbine nacelle chassis fatigue damage dataset, released by Goldwind, was applied to validate the proposed method. The results demonstrated that the SVR-FDA yielded the highest assessment accuracy for the lifetime ESA, as compared with four popular machine learning algorithms, including the least absolute shrinkage and selection operator, random forest, extreme gradient boosting, and deep neural network.