Integration of Wind and PV Systems Using Genetic-Assisted Artificial Neural Network

作     者：E.Jessy Mol M.Mary Linda 

作者机构：Ponjesly College of EngineeringNagerkovilKanyakumari629003India 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第35卷第2期

页      面：1471-1489页

核心收录：

学科分类：08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Wind energy converter double fed induction generator field oriented control GA-ANN MPPT DC-link voltage control 

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 *** 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 *** proposed approach delivers better per-formance in the compensation of real and reactive power along with the DC link voltage *** 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 *** source current THD is observed as 1.93%and 2.4%for simulation and hardware implementation respectively.