Integration of Wind and PV Systems Using Genetic-Assisted Artificial NeuralNetwork

摘要

The prominence of Renewable Energy Sources(RES)in the process of power generation is exponentially increased in the recent days since these sources assist in minimizing the environmental contamination.A grid-tied DFIG(Doubly Fed Induction Generator)based WECS(Wind Energy Conversion System)is introduced in this work,in which a Landsman converter is implemented to impro-vise the output voltage of PV without anyfluctuations.A novel GA(Genetic Algorithm)assisted ANN(Artificial Neural Network)is employed for tracking the Maximum power from PV.Among the rotor and grid side controllers,the for-mer is implemented by combining the statorflux with d-q reference frame and the latter is realized by the PI controller.The proposed approach delivers better per-formance in the compensation of real and reactive power along with the DC link voltage control.The controlling mechanism is verified in both MATLAB and experimental bench setupby using an emulated wind turbine for the concurrent control of DC link potential,active and reactive powers.The source current THD is observed as 1.93%and 2.4%for simulation and hardware implementation respectively.