Enhancing steady-state and dynamic performance of wind turbine doubly fed induction generator using AI optimization approaches with adaptive PI controllers

Owing to the anticipated rise in the wind energy production and its efficient integration with the grid, wind power ranks among the most appealing renewable energy sources globally. Several robust control schemes are necessary for enhancing the overall system efficiency, power quality and ensuring a more reliable operation of the controller. Thus, the bonobo optimizer (BO) is employed to determine the optimal controllers’ parameters for grid connected doubly fed induction generator (DFIG) based wind energy conversion system (WECS). The proposed optimal performance of WECS is developed by controlling 19 controller parameters to minimize the torque ripple and maximum power point tracking (MPPT) of WT-DFIG. The optimization variables are the parameters of the proportional-integral (PI) of rotor and grid sides converters, capacitance and voltage of DC bus, the values of a delta connected LC filter for rotor side converter and L filter for grid side converter. The optimum power curve and pitch angle control are estimated as the reference values of WT-DFIG system. The BO results are validated by comparing them with particle swarm optimizer (PSO). The Matlab environment is used to simulate the 2.4 MW DFIG based WECS.