Abstract

AbstractTMD plays a great role in the wind turbine vibration control under various working conditions, which will reduce the fatigue damage and prolong the service life. However, previous research and design generally adopted the finite element simulation, which is not consistent with actual project in model and load calculation, causing difficulty when applying technology transformation. Meanwhile, the damping performance of TMD in all directions and limitation of installation space in nacelle need to be studied further. Consequently, this study proposes a new TMD design, and then completes the combined model of turbine and TMD in the commercial software SIMPACK. Innovatively, typical working conditions are analyzed to establish the radial basis function neural network, which contribute to global optimization of TMD by using genetic algorithm. Besides, comparison of simulation results with and without dampers is given to verify the performance of designed TMD, taking the reduction rate of time history standard deviation as the index to evaluate damping effect. The final results indicate that the installation of optimized TMD can reduce the average force at tower bottom by 23.84% and the average bending moment by 18.29%. The results have been applied to TMD design in real engineering.KeywordsWind turbineTMDVibration control

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