Title :
Neural-network-based sensorless maximum wind energy capture with compensated power coefficient
Author :
Li, Hui ; Shi, K.L. ; McLaren, P.G.
Author_Institution :
Dept. of Electr. & Comput. Eng., Florida A&M Univ., Tallahassee, FL, USA
Abstract :
This paper describes a small wind generation system where neural network principles are applied for wind speed estimation and robust control of maximum wind power extraction against potential drift of wind turbine power coefficient curve. The new control system will deliver maximum electric power to a customer with light weight, high efficiency, and high reliability without mechanical sensors. The concept has been developed and analyzed using a turbine directly driven permanent-magnet synchronous generator (PMSG). In addition, the proposed method is applied to a 15-kW variable-speed cage induction machine wind generation (CIWG) system. The simulation studies of a PMSG small wind generation system and experimental results of a CIWG are provided to verify the validity of the method.
Keywords :
asynchronous generators; neurocontrollers; permanent magnet generators; power generation control; robust control; synchronous generators; wind power plants; wind turbines; 15 kW; compensated power coefficient; maximum power coefficient curve; mechanical sensors; neural network; permanent-magnet synchronous generators; robust control; sensorless maximum wind energy capture; turbines; variable-speed cage induction machine wind generator; wind generation system; wind speed estimation; Energy capture; Induction generators; Neural networks; Power generation; Power system reliability; Wind energy; Wind energy generation; Wind power generation; Wind speed; Wind turbines; Neural networks; permanent-magnet generators; wind turbine;
Journal_Title :
Industry Applications, IEEE Transactions on
DOI :
10.1109/TIA.2005.858282
