Posters

Abstract

In the wind energy sector, the push for tighter safety margins in new turbine designs and the extension of operational lifetimes for existing turbines have heightened the need for effective load-reduction processes. Addressing this challenge, DEIF has developed an innovative automated algorithm to detect and mitigate aerodynamic rotor imbalances, advancing upon recent scientific research ( [1] [2]). Aerodynamic imbalances in wind turbines are a significant source of increased vibrational stress and annual energy production (AEP) losses. Field studies ( [3]) reveal that approximately 45% of turbines suffer from intolerable aerodynamic rotor imbalances, caused by relative blade angle deviation. These numbers underscore the critical importance of an effective rotor balancing strategy. Aerodynamic imbalances stem from uneven thrust forces on the blades. While external factors such as structural damage, erosion, or ice accretion can contribute, the predominant causes include manufacturing tolerances, sensor drift, and calibration errors, all of which result in misaligned blade angles. Unlike mass imbalances, which require downtime for counterweight installation, aerodynamic imbalances can be corrected dynamically by applying pitch angle offsets to the blades. DEIF's algorithm leverages the well-known relationship between the 1p-harmonic response of the tower-top fore-aft acceleration and aerodynamic imbalance. The amplitude of the response correlates with imbalance severity, while the phase identifies its location. Running directly on the wind turbine controller, the method iteratively applies pitch corrections to reduce the vibrational response and achieve optimal rotor balance. Rather than estimating a correction from just one measurement, this iterative process allows the application of the algorithm without any pre-knowledge about the turbine type, making it widely applicable without adaptions. The process begins with two 10-minute measurement periods. The first captures the baseline 1p-harmonic response without any pitch adjustments, while the second introduces a known pitch offset (e.g., +1° on Blade #1). By analyzing changes in amplitude and phase between these iterations, the algorithm calculates the optimal corrective pitch angles for each blade. If residual imbalances persist, the process repeats with the updated corrections. This approach offers several advantages: it eliminates the need for additional sensors, minimizes turbine downtime, and provides a cost-effective solution for operators. DEIF has successfully deployed this method on Suzlon and Senvion turbine platforms, achieving significant performance improvements and extending the operational lifespan of multiple wind turbines. By integrating automated rotor balancing into turbine control systems, operators can mitigate vibrational stress, enhance AEP, and support the sustainable growth of wind energy. This innovation exemplifies DEIF's commitment to advancing wind turbine performance through practical, data-driven solutions. [1]  M. Bertelé, C. Botasso and S. Cacciola, Automatic detection and correction of pitch misalignment in wind turbine rotors, Wind Energy Science, 2018.  [2]  C. Reichmann, Estimation and correction methods towards rotor balancing of a wind turbine, Alpen-Adria University Klagenfurt, 2020.  [3]  A. Grunwald, C. Heilmann and M. Melsheimer, Improved quality of rotor balancing measurement through criteria from the guideline VDI-3834-1:2015, WindEurope Summit 2016, 2016.