Designing localized MPPT for PV systems using fuzzy-weighted extreme learning machine

Du, Yang, Yan, Ke, Ren, Zixiao, and Xiao, Weidong (2018) Designing localized MPPT for PV systems using fuzzy-weighted extreme learning machine. Energies, 11 (10). 2615.

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A maximum power point tracker (MPPT) should be designed to deal with various weather conditions, which are different from region to region. Customization is an important step for achieving the highest solar energy harvest. The latest development of modern machine learning provides the possibility to classify the weather types automatically and, consequently, assist localized MPPT design. In this study, a localized MPPT algorithm is developed, which is supported by a supervised weather-type classification system. Two classical machine learning technologies are employed and compared, namely, the support vector machine (SVM) and extreme learning machine (ELM). The simulation results show the outperformance of the proposed method in comparison with the traditional MPPT design.

Item ID: 58367
Item Type: Article (Research - C1)
ISSN: 1996-1073
Keywords: maximum power point tracker; solar irradiance classification system; extreme learning machine; support vector machine
Copyright Information: © 2018 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Funders: National Natural Science Foundation of China (NNSFC), Xi’an Jiaotong-Liverpool University (XJTLU), Jiangsu University (JU) Science and Technology Program (S&T) Programme
Projects and Grants: NNSFC 61850410531, NNSFC 61803315, XJTLU RDF-17-01-28, XJTLU KSF-A-11, JU S&T Program SBK2018042034
Date Deposited: 28 May 2019 23:41
FoR Codes: 40 ENGINEERING > 4008 Electrical engineering > 400803 Electrical energy generation (incl. renewables, excl. photovoltaics) @ 50%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 50%
SEO Codes: 85 ENERGY > 8505 Renewable Energy > 850504 Solar-Photovoltaic Energy @ 100%
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