A Hybrid Control Method for Maximum Power Point Tracking (MPPT) in Photovoltaic Systems

被引：42

作者：

Vincheh, Mahdi Rajabi ^{[1
]}

Kargar, Abbas ^{[1
]}

Markadeh, Gholamreza Arab ^{[1
]}

机构：

[1] Shahrekord Univ, Fac Engn, Shahrekord, Iran

来源：

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING | 2014年 / 39卷 / 06期

关键词：

Boost converter; Fuzzy logic controller; Genetic algorithm; MPPT; Neural network; PV cell; NEURAL-NETWORK;

D O I：

10.1007/s13369-014-1056-0

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Solar photovoltaic (PV) energy has witnessed growth in the past decade. Nowadays, PV energy systems have proved to be effective methods for renewable energy resources with minimum environmental impacts. Due to these environmental and economic benefits, PV systems are being widely deployed as distributed energy resources in distribution generation systems or microgrids. Maximum power point tracking (MPPT) algorithms have an important role to play due to optimization performance in these systems. In this paper, PV array output voltage has been optimized by increasing the MPPT algorithm performance. A new hybrid fuzzy-neural MPPT controller is proposed. Training data in neural network are optimized by genetic algorithm. The proposed controller is simulated and studied using MATLAB software. The obtained results show superior capability of the suggested method in MPP tracking under rapid fluctuation of atmospheric conditions and converter load.

引用

页码：4715 / 4725

页数：11

共 20 条

[1] High-Performance Adaptive Perturb and Observe MPPT Technique for Photovoltaic-Based Microgrids [J].

Abdelsalam, Ahmed K. ;

Massoud, Ahmed M. ;

Ahmed, Shehab ;

Enjeti, Prasad N. .

IEEE TRANSACTIONS ON POWER ELECTRONICS, 2011, 26 (04) :1010-1021

[2]

Bekker B, 2004, IEEE AFRICON, P1125

[3] Comparison of photovoltaic array maximum power point tracking techniques [J].

Esram, Trishan ;

Chapman, Patrick L. .

IEEE TRANSACTIONS ON ENERGY CONVERSION, 2007, 22 (02) :439-449

[4]

Hadji S., 2011, 2011 7th International Workshop on Systems, Signal Processing and their Applications (WOSSPA 2011), P43, DOI 10.1109/WOSSPA.2011.5931408

[5] Neural network based estimation of maximum power generation from PV module using environmental information [J].

Hiyama, T ;

Kitabayashi, K .

IEEE TRANSACTIONS ON ENERGY CONVERSION, 1997, 12 (03) :241-246

[6] IDENTIFICATION OF OPTIMAL OPERATING POINT OF PV MODULES USING NEURAL-NETWORK FOR REAL-TIME MAXIMUM POWER TRACKING CONTROL [J].

HIYANA, T ;

KOUZUMA, S ;

IMAKUBO, T .

IEEE TRANSACTIONS ON ENERGY CONVERSION, 1995, 10 (02) :360-367

[7]

Jie L, 2011, CHIN CONT DECIS CONF, P1851, DOI 10.1109/CCDC.2011.5968501

[8] High-Speed Maximum Power Point Tracker for Photovoltaic Systems Using Online Learning Neural Networks [J].

Kohata, Yasushi ;

Yamauchi, Koichiro ;

Kurihara, Masahito .

JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS, 2010, 14 (06) :677-682

[9] A novel maximum power point tracking method for PV module integrated converter [J].

Koizumi, H ;

Kurokawa, K .

2005 IEEE 36TH POWER ELECTRONIC SPECIALISTS CONFERENCE (PESC), VOLS 1-3, 2005, :2081-2086

[10] Training data optimization for ANNs using genetic algorithms to enhance MPPT efficiency of a stand-alone PV system [J].

Kulaksiz, Ahmet Afsin ;

Akkaya, Ramazan .

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2012, 20 (02) :241-254

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