Adaptive Neuro-Fuzzy Inference System-Based Maximum Power Tracking Controller for Variable Speed WECS

This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller for grid-connected doubly fed induction generator (DFIG)-based wind energy conversion systems (WECS). It aims at extracting from the by peak regardless of speed. The proposed MPPT implements ANFIS approach with a backpropagation algorithm. rotor speed acts as input to and torque reference controller’s output, which further inputs side converter’s control loop rotor’s actual adjusting duty ratio converter. grid partition method generates membership functions uniformly partitioning variable ranges creating single-output Sugeno fuzzy system. neural network trained according alter initial functions. simulation results have been validated on 2 MW turbine using MATLAB/Simulink environment. performance is tested under various circumstances compared conventional proportional–integral controller. study shows that WECS can operate its optimum wide range