Degradation assessment of wind turbine based on additional load measurements

Yifei Wang, Rui He, Wilhelm Schünemann, Zhigang Tian, Jinqiu Pan and Ralf Schelenz

Renewable Energy, 2024, vol. 235, issue C

Abstract: In recent years, there has been an increasing focus on the degradation assessment of wind turbines using supervisory control and data acquisition (SCADA) data, establishing condition monitoring as the preferred approach. However, SCADA data primarily focuses on general condition variables, such as temperature, while overlooking critical metrics like load factors for assessing the mechanical properties of wind turbines. The load parameters can be utilized to assess the mechanical stresses and strains experienced by wind turbine components, thereby offering more precise information on degradation. To address this gap, we explore the feasibility of implementing additional load monitoring for the degradation assessment of wind turbines. Specifically, we design a cost-effective load sensor system for collecting load data from wind turbines. On this basis, a novel degradation assessment method that incorporates additional load data and a nonlinear dynamic state-space neural network model is proposed to extract degradation information from wind turbines. Then, we introduce a dynamic degradation index, derived through trend analysis, which effectively captures and quantifies the degradation levels of wind turbines over time. Finally, a case study using a dataset collected from a real wind farm is provided to validate the proposed method. The results demonstrate a significant enhancement in degradation assessment. By incorporating additional load data, the proposed method exhibits heightened responsiveness to degradation levels and more effectively captures the progression of degradation trends.

Keywords: Degradation assessment; Supervisory control and data acquisition (SCADA) data; Measurement data; Wind turbine (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:235:y:2024:i:c:s0960148124013399

DOI: 10.1016/j.renene.2024.121271

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