LAPSE:2019.0293
Published Article
LAPSE:2019.0293
Global Maximum Power Point Tracking (MPPT) of a Photovoltaic Module Array Constructed through Improved Teaching-Learning-Based Optimization
Kuei-Hsiang Chao, Meng-Cheng Wu
February 27, 2019
The present study proposes a maximum power point tracking (MPPT) method in which improved teaching-learning-based optimization (I-TLBO) is applied to perform global MPPT of photovoltaic (PV) module arrays under dissimilar shading situations to ensure the maximum power output of the module arrays. The proposed I-TLBO enables the automatic adjustment of teaching factors according to the self-learning ability of students. Incorporating smart-tracking and self-study strategies can effectively improve the tracking response speed and steady-state tracking performance. To evaluate the feasibility of the proposed I-TLBO, a HIP-2717 PV module array from Sanyo Electric was employed to compose various arrays with different serial and parallel configurations. The arrays were operated under different shading conditions to test the MPPT with double, triple, or quadruple peaks of power-voltage characteristic curves. Boost converters were employed with TMS320F2808 digital signal processors to test the proposed MPPT method. Empirical results confirm that the proposed method exhibits more favorable dynamic and static-state response tracking performance compared with that of conventional TLBO.
Keywords
maximum power point tracking, partial module shading, photovoltaic module array, teaching-learning-based optimization
Suggested Citation
Chao KH, Wu MC. Global Maximum Power Point Tracking (MPPT) of a Photovoltaic Module Array Constructed through Improved Teaching-Learning-Based Optimization. (2019). LAPSE:2019.0293
Author Affiliations
Chao KH: Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
Wu MC: Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
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Journal Name
Energies
Volume
9
Issue
12
Article Number
E986
Year
2016
Publication Date
2016-11-25
Published Version
ISSN
1996-1073
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Original Submission
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PII: en9120986, Publication Type: Journal Article
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LAPSE:2019.0293
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doi:10.3390/en9120986
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Feb 27, 2019
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Feb 27, 2019
 
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Calvin Tsay
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