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LIDAR‐assisted radial basis function neural network optimization for wind turbines
Author(s) -
Han Bing,
Zhou Lawu,
Zhang Zhiwen
Publication year - 2018
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22514
Subject(s) - lidar , wind power , turbine , wind speed , ranging , radial basis function , controller (irrigation) , torque , artificial neural network , pitch control , feedforward neural network , feed forward , renewable energy , automotive engineering , drivetrain , computer science , radial basis function network , engineering , marine engineering , environmental science , control engineering , meteorology , aerospace engineering , artificial intelligence , remote sensing , geology , electrical engineering , telecommunications , thermodynamics , agronomy , physics , biology
Increasing use of large commercial wind turbines motives energy efficiency improvement and fatigue load mitigation in wind turbines. Advanced control methods designed with remote sensing techniques are considered as promising solutions. In this paper, we design a radial basis function neural network feedforward control based on light detection and ranging (LIDAR) measurement. In this control method, the measurements of wind‐speed disturbance from LIDAR are used to train weights online in a neural network for optimizing the blade pitch angle and electromagnetic torque in a wind turbine, which is helpful in tracking the maximum wind energy and alleviate fatigue loads. The effectiveness of the proposed controller is validated with the National Renewable Energy Laboratory's typical three‐blade wind turbine. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.