Load identification of a 2.5 MW wind turbine tower using Kalman filtering techniques and BDS data. (15th April 2023)

• Record Type: Journal Article
• Authors: Wei, Da; Li, Dongsheng; Jiang, Tao; Lyu, Pin; Song, Xiaofei
• Is Part Of: Engineering structures. Volume 281(2023)
• Publication Date: 2023-04-15
• Subjects: Wind turbine -- Thrust and bending moment estimation -- BDS -- Health monitoring -- Kalman filter; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2023.115763
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: The bending moment estimation method based on PM-AKF is established. The force estimation algorithm is firstly applied to an actual wind turbine structure using acceleration sensors and BeiDou Navigation Satellite System. A new load monitoring strategy for wind turbine thrust and tower bending moment is proposed. The static load fitting equation of the wind turbine is derived. Abstract: The rapid increase in the number of wind turbine installations and operations, is increasing the importance of the health monitoring of these wind turbines. To assess the remaining service life of a wind turbine, continuous monitoring of the internal forces acting on the critical locations of fatigue in the structure is required. However, a large number of sensors are usually required for the comprehensive determination of the internal force at a site-specific location; additionally, these sensors cannot be placed in locations with strong stress or strain gradients. Therefore, in this paper, a strategy for estimating the thrust at the tower top and the bending moment at any position of the wind turbine tower is proposed, which only requires a limited number of acceleration sensors and the BeiDou Navigation Satellite System (BDS). The estimated load includes static and dynamic components. The former is calculated by fitting the derived function and the BDS data, and the latter is estimated by the time-domain inverse method using the Kalman filter and with acceleration sensors. The performance of the proposed strategy is validated in two case studies, including simulated data and recorded data from a 2.5 MW onshore wind turbine located in China. The results show that the strategy can produce an estimation of thrust and bending moment, with good accuracy.