Neural network modeling of a doubly fed induction generator wind turbine system

A wind power plant is an energy conversion system consisting of wind turbine, rotor, gear and doubly-fed induction generator respectively. It is a complex multivariable system associated with severe nonlinearity, uncertainties and multivariable couplings. In many cases, it is almost impossible to build a mathematical model of the system using conventional analytic methods. The paper presents our recent work in modeling of a 1.5MW doubly-fed induction generator. Using on-site measurement data, two different structures of neural networks are employed to model the doubly-fed induction generator. The method is compared with the typical recursive least squares (RLS) method, which obviously demonstrated the merit of efficiency of the neural networks in modeling of the 1.5MW doubly-fed induction generator.