摘要
In the last decade,artificial intelligence(AI)techniques have been extensively used for maximum power point tracking(MPPT)in the solar power system.This is because conventional MPPT techniques are incapable of tracking the global maximum power point(GMPP)under partial shading condition(PSC).The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points(MPPs).The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT.The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits.In general,all of the AI-based MPPT techniques exhibit fast convergence speed,less steady-state oscillation and high efficiency,compared with the conventional MPPT techniques.However,the AI-based MPPT techniques are computationally intensive and costly to realize.Overall,the hybrid MPPT is favorable in terms of the balance between performance and complexity,and it combines the advantages of conventional and AI-based MPPT techniques.In this paper,a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results.The merits,open issues and technical implementations of AI-based MPPT techniques are evaluated.We intend to provide new insights into the choice of optimal AI-based MPPT techniques.
基金
supported by the School of Engineering
Monash University Malaysia
作者简介
Kah Yung Yap received the B.Eng.degree(Hons.)in electrical and electronic engineering from University College Sedaya International(UCSI),Cheras,Wilayah Persekutuan Kuala Lumpur,Malaysia.He is currently pursuing the Ph.D.degree in electrical and computer systems engineering(ECSE)at Monash University,Subang Jaya,Selangor,Malaysia under Graduate Research Merit Full Scholarship.He is a Graduate Engineer of the Board of Engineers Malaysia(BEM)and a Graduate Member of the Institution of Engineers Malaysia(IEM).He was awarded the 2017 IEM Gold Award Medal for the Best Engineering Student.He is also a graduate student member of The Institute of Electrical and Electronics Engineers(IEEE),a society member in IEEE Power&Energy Society(PES)and a member of The Institution of Engineering and Technology(MIET).His current research interests include machine learning based synchronverters,grid-connected solar photovoltaic systems,and power quality improvement.e-mail:kah.yap@monash.edu;Corresponding author:Charles R.Sarimuthu received the B.Eng.and M.Eng.degrees in electrical engineering from the University of Malaya,Wilayah Persekutuan Kuala Lumpur,Malaysia,in 2006 and 2010,respectively,and the Ph.D.degree in electrical power engineering from National Energy University,Kajang,Malaysia,in 2019.He is a member of the Institution of Engineers Malaysia(IEM)and a Registered Engineer of the Board of Engineers Malaysia(BEM).In 2018,he joined as a Lecturer with the Department of Electrical and Computer Systems Engineering,School of Engineering,Monash University,Subang Jaya,Malaysia.His research interests include power system,power quality,renewable energy,and smart grid.e-mail:Charles.Raymond.Sarimuthu@monash.edu;Joanne Mun-Yee Lim received the B.E.degree(Hons.)from Monash University,Subang Jaya,Malaysia,in 2008,the M.E.degree from the University of Malaya,Wilayah Persekutuan Kuala Lumpur,Malaysia,in 2011,and the Ph.D.degree in engineering from Multimedia University,Cyberjaya,Malaysia,in 2016.She received an Outstanding Dissertation Award from the IEEE Malaysia ComSoc/Vehicular Technology Society Joint Chapter for her Ph.D.work.She is also a member of The Institution of Engineers Malaysia and a Professional Engineer of the Board of Engineers Malaysia.She is currently a Lecturer with Monash University.Her research interests include vehicular Ad-hoc networks,mobile IPv6-based network,unmanned aerial vehicle,artificial intelligence,control system,optimization scheme,robotic design,and renewable energy application.e-mail:joanne.lim@monash.edu
