A variable speed wind generator maximum power tracking based on adaptative neuro-fuzzy inference system

The power from wind varies depending on the environmental factors. Many methods have been proposed to locate and track the maximum power point (MPPT) of the wind, such as the fuzzy logic (FL), artificial neural network (ANN) and neuro-fuzzy. In this paper, a variable-speed wind-generator maximum-power-point-tracking (MPPT) based on adaptative neuro-fuzzy inference system (ANFIS) is presented. It is designed as a combination of the Sugeno fuzzy model and neural network. The ANFIS model is used to predict the optimal speed rotation using the variation of the wind speed as the input. The wind energy conversion system (WECS) employing a permanent magnet synchronous generator connected to a DC bus using a power converter is presented. A wind speed step model was used in the design phase. The performance of the WECS with the proposed ANFIS controller is tested for fast wind speed variation. Simulation results showed the possibility of achieving maximum power tracking for the wind and output voltage regulation for the DC bus simultaneously with the ANFIS controller. The results also proved the good response and robustness of the control system proposed.