Offshore wind turbine (OWT) upsizing has become an inevitable trend. Compared with small OWTs, large OWT rotors exert greater horizontal loads and bending moments on the tower. These factors make a large OWT tower more prone to geometric nonlinear deformations, which can affect the normal operation of a wind turbine and even lead to damage to the wind turbine tower. Therefore, monitoring the deformation of large OWT towers is necessary. Shape sensing is a technique that uses surface strains to reconstruct deformed shapes. Currently, there is a lack of effective methods for geometric nonlinearity deformation shape sensing. In addition, the tower of OWT is a variable cross-section structure. The influence of varying cross-sections further increases the difficulty of developing a large deformation reconstruction algorithm. To solve this problem, this paper proposes a new method called rotation angle approximation (RAA). This method establishes a least-squares error functional using analytic curvature and section curvature. The rotation angles of the tower axis are obtained via this function. The deformed shape is then predicted via the boundary conditions and the rotation angles along the tower axis. Numerical simulations demonstrated this method can accurately predict the large deformations of a tower under different load conditions.
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