A Maximum Power Point Tracking Method Based on Extension Neural Network for PV Systems

被引：0
|
作者：
Chao, Kuei-Hsiang [1 ]
Li, Ching-Ju [1 ]
Wang, Meng-Huei [1 ]
机构：
[1] Natl Chin Yi Univ Technol, Dept Elect Engn, Taichung, Taiwan
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2009, PT 1, PROCEEDINGS | 2009年 / 5551卷
关键词：
Maximum power point tracking (MPPT); Perturbation and observation (P&O) method; Incremental conductance (INC) method; Photovoltaic (PV) system; Extension neural network (ENN); FUZZY CONTROLLER; MPPT METHOD;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, it maximum power point tracking (MPPT) technique based on extension neural network (ENN) was proposed to make full utilization of photovoltaic (PV) array Output power which depends on solar insolation and ambient temperature. The proposed ENN MPPT algorithm can automatically adjust the step size to track the PV array maximum power point (MPP). Compared with the conventional fixed step size perturbation and observation (P&O) and incremental conductance (INC) methods, the presented method is able to effectively improve the dynamic response and steady state performance of the PV systems Simultaneously. A theoretical analysis and the designed principle of the proposed method are described in detail. And some simulation results are made to demonstrate the effectiveness of the proposed MPPT method.
引用
下载
收藏
页码：745 / 755
页数：11
相关论文
共 50 条
  • [21] Maximum Power Point Tracking control of PV system for DC motors drive with Neural Network
    Ko, Jae-Sub
    Jung, Byung-Jin
    Park, Ki-Tae
    Choi, Chung-Hoon
    Chung, Dong-Hwa
    2008 INTERNATIONAL CONFERENCE ON SMART MANUFACTURING APPLICATION, 2008, : 514 - 519
  • [22] A Neural Network-Based Rapid Maximum Power Point Tracking Method for Photovoltaic Systems in Partial Shading Conditions
    Adi Kurniawan
    Eiji Shintaku
    Applied Solar Energy (English translation of Geliotekhnika), 2020, 56 (03): : 157 - 167
  • [23] A New Maximum Power Point Tracking Method for PV System
    Gao, Zhiqiang
    Li, Song
    Zhou, Xuesong
    Ma, Youjie
    Zhao, Jian
    2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2017, : 544 - 548
  • [24] Maximum Power Point Tracking Control using Neural Network for Photovoltaic Systems
    Talbi, Mourad
    Makhlouf, Olfa
    Mensia, Nawel
    Ezzaouia, Hatem
    2019 10TH INTERNATIONAL RENEWABLE ENERGY CONGRESS (IREC), 2019,
  • [25] The Maximum Power Point Tracking for PV Systems: Introduction to The Input Characteristic Impedance Method
    Zanotti, J. W.
    Dos Santos, W. M.
    Martins, D. C.
    2014 IEEE 5TH INTERNATIONAL SYMPOSIUM ON POWER ELECTRONICS FOR DISTRIBUTED GENERATION SYSTEMS (PEDG), 2014,
  • [26] A Maximum Power Point Tracking Method for PV Systems Supplying a Periodic Nonlinear Load
    Hsu, Chung-Ti
    Peng-Li
    Sheng, Su
    Lehman, Brad
    2013 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE), 2013, : 2771 - 2777
  • [27] Comparison of fuzzy logic and neural network in maximum power point tracker for PV systems
    Ben Salah, Chokri
    Ouali, Mohamed
    ELECTRIC POWER SYSTEMS RESEARCH, 2011, 81 (01) : 43 - 50
  • [28] Dynamic Neural Control for Maximum Power Point Tracking of PV System
    Dounis, Anastasios I.
    Kofinas, P.
    Alafodimos, C.
    Tseles, D.
    ELEVENTH SYMPOSIUM ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING (NEUREL 2012), 2012,
  • [29] A Dual Tangent Based Algorithm for Maximum Power Point Tracking of Solar PV Systems
    Jain, Anubhav
    Jain, Chinmay
    Singh, Bhim
    Kamal-Al-Haddad
    Chandra, Ambrish
    IECON 2015 - 41ST ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2015, : 601 - 606
  • [30] A Hybrid PSO and GSA -Based Maximum Power Point Tracking Algorithm for PV Systems
    Dhas, B. Goldvin Sugirtha
    Deepa, S. N.
    2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMPUTING RESEARCH (ICCIC), 2013, : 309 - 312
12345