An Intelligent Maximum Power Control for Transverse Flux Permanent Magnet Generator

This research is worked on the concept of the converter-controlled permanent magnet generator for wind power generation system. For nonlinear dynamics with unknown nonlinearities of the system, a neural network controller for achieving maximum power tracking as well as output voltage regulation for a wind energy conversion system (WECS) employing a transverse flux permanent magnet synchronous generator (TFPMG) is proposed. The TFPMG supplies loads via a bridge rectifier, a buck-boost and a Cuk converter. Adjusting the switching frequencies of the two converters achieve maximum power tracking and output voltage regulation. The on-times of the switching devices of the two converters are supplied by the neural network controller (NNC). The effects of sudden changes in wind speed and reference voltage on the performance of the NNC are investigated. Simulation experiments showed the possibility of achieving maximum power tracking and output voltage regulation. The results also proved the fast response and robustness of the control system.