Abstract
Based on the mathematical model of Permanent magnet synchronous generator (PMSG), maximum wind power tracking control strategy without wind speed detection is analyzed and a controller based on cloud RBF neural network and approximate dynamic programming is designed to track the maximum wind power point. Optimal power-speed curve and vector control principles are used to control the electromagnetic torque by approximate dynamic programming controller to adjust the voltage of stator, so the speed of wind turbine can be operated at the optimal speed corresponding to the best power point. Cloud RBF neural network is adopted as the function approximation structure of approximate dynamic programming, and it has the advantage of the fuzziness and randomness of cloud model. Simulation results show that the method can solve the optimal control problem of complex nonlinear system such as wind generation and track the maximum wind power point accurately.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
