Data‐driven modelling of a doubly fed induction generator wind turbine system based on neural networks

In a wind power system, the wind turbine captures wind energy and converts it into electric energy through a coupled rotating generator. This renewable energy conversion system usually consists of a wind turbine, rotor, gearbox and mostly a doubly fed induction generator (DFIG). It is a complex non‐linear multi‐input multi‐output system with many uncertain factors. Meanwhile, the dynamics of the system is quite dependent on the wind velocity. Traditional analytical methods are quite difficult to model such a complex system. The recently developed data‐driven method can be a suitable modelling technique for such system. Using a large amount of input–output on‐line measurement data from the selected months, neural networks and neuro‐fuzzy networks are fully utilised to model the DFIG. Detailed analysis and comparisons with the classical system identification techniques are addressed to show the advantages of the data‐driven DFIG modelling approach.