Optimization of wind turbine TMD under real wind distribution countering wake effects using GPU acceleration and machine learning technologies.

Excessive fore-aft vibrations of wind turbine tower are the major reason for tower collapsing, which can be evaluated using the equivalent fatigue load (EFL). Wake effects are generated by the former wind turbines on the latter ones, and can greatly increase EFL and reduce lifetime of the latter ones, but they were seldom considered. Consequently, this study calculated EFL countering the wake effects under real wind distributions. The effects of wind turbine spacing on EFL indicate that the wake effects between wind turbines with spacing of approximately 2 times of rotor radius should be considered. Subsequently, tuned mass damper (TMD) was placed in the nacelle to passively control the fore-aft vibrations. An optimization tool for TMDs was developed based on the radial basis function neural network and genetic algorithm, which was compared with the theoretical equations and parametric analysis. GPU acceleration technology was utilized. Numerical results show that, under real wind distributions, the TMDs obtain at least 40.1% EFL reduction, which is 8.9% higher than the theoretically optimized one. Importantly, the GPU-based codes can run 2001 times faster than the CPU-based ones, and the optimization tool can reduce 44.9% computational time further. The codes are made available for other researchers. • PyGAOWT is validated against commonly used GH-bladed and obtains well agreements. • GPU-accelerated PyGAOWT can run 2001 times faster than CPU version and is opened. • RBFNN-GA-based optimization tool performs better and reduces 45% time compared with parametric analysis. • For wind turbines with spacing of about 2 times of rotor radius, TMDs should be applied. • TMD is optimized under real wind distribution instead of white noise with wake effects countered. [ABSTRACT FROM AUTHOR]