Maximum power point tracking for a photovoltaic water pumping system with sliding mode control and fuzzy wavelet network

被引:0
作者
Sefriti, Bouchra. [1 ]
Dahhani, Omar. [1 ]
Boumhidi, Ismail. [1 ]
机构
[1] Univ Sidi Mohammed ben Abdellah, Lab Elect Signals Syst & Informat, Fac Sci Dhar Mehraz, Fez Atlas, Morocco
来源
2015 INTELLIGENT SYSTEMS AND COMPUTER VISION (ISCV) | 2015年
关键词
Sliding mode control; fuzzy wavelet network; photovoltaic pumping system; maximum power point tracking;
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
This paper presents a maximum power point tracking method (MPPT) that combines fuzzy wavelet network with sliding mode control for a photovoltaic pumping system. For the best use, the photovoltaic (PV) generator must operate at its maximum power point (MPP). SMC uses a high switching gain to cover the neglected uncertainties in the system model. However, the SMC produces chattering phenomenon due to the higher needed switching gain, in the presence of large uncertainties. In order to reduce this gain, fuzzy wavelet network (FWN) technique is used in this work to predict the unknown part of the PV pumping system model, which enables the well description of the real system.
引用
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页数:7
相关论文
共 12 条
[1]   Fuzzy wavelet neural networks for identification and control of dynamic plants - A novel structure and a comparative study [J].
Abiyev, Rahib Hidayat ;
Kaynak, Okyay .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2008, 55 (08) :3133-3140
[2]  
Balasubramanian G., 2014, INT J DEV RES, V4, P635
[3]   Assessment of Perturb and Observe MPPT Algorithm Implementation Techniques for PV Pumping Applications [J].
Elgendy, Mohammed A. ;
Zahawi, Bashar ;
Atkinson, David J. .
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2012, 3 (01) :21-33
[4]  
ElJouni A, 2007, C SYST CONTR CSC 200, P1
[5]  
Farhat Mayssa, 2011, Science Academy Transactions on Renewable Energy Systems Engineering and Technology, V1, P29
[6]  
Hamrouni N., 2008, Revue des Energies Renouvelables, V11, P95
[7]   A THEORY FOR MULTIRESOLUTION SIGNAL DECOMPOSITION - THE WAVELET REPRESENTATION [J].
MALLAT, SG .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1989, 11 (07) :674-693
[8]   Local linear wavelet neural network for breast cancer recognition [J].
Senapati, M. R. ;
Mohanty, A. K. ;
Dash, S. ;
Dash, P. K. .
NEURAL COMPUTING & APPLICATIONS, 2013, 22 (01) :125-131
[9]  
Utkin VI., 2013, Sliding modes in control and optimization
[10]  
Valenciaga F, 2000, INT J ENERG RES, V24, P151, DOI 10.1002/(SICI)1099-114X(200002)24:2<151::AID-ER569>3.0.CO