Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system

Abstract: This paper proposes a precise real-time wind speed estimation method and sensorless control for variable-speed variable-pitch wind turbine power generation system (WTPGS). The wind speed estimation is realized by a nonlinear input–output mapping extreme learning machine (ELM). A specific design characteristic of the wind turbine is used for improving the mapping accuracy with considering the variable pitch angle. Moreover, since the design is independent of the environmental air density, the proposed ELM wind speed estimation method is robust to the air density variations. The estimated wind speed is then used to determine the optimal rotational speed command for maximum power point tracking. A fast and effective ELM mapping based pitch controller is proposed too when the WTPGS operates in its high wind speed region. The ELM pitch controller can act much faster and more precise than conventional pitch controllers. Furthermore, the complicated design precess for the parameters of conventional pitch controllers will be avoided in the proposed method. The effectiveness of the proposed methods are verified both by simulations and experiments on a WTPGS installed with Permanent Magnet Synchronous Generator (PMSG). [Copyright &y& Elsevier]