Designing Localized MPPT for PV Systems Using Fuzzy-Weighted Extreme Learning Machine

Yang Du, Ke Yan, Zixiao Ren and Weidong Xiao
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Yang Du: Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
Ke Yan: College of Information Engineering, China Jiliang University, Hangzhou 310018, China
Zixiao Ren: Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
Weidong Xiao: School of Electrical and Information Engineering, The University of Sydney, Western Avenue, Sydney, NSW 2006, Australia

Energies, 2018, vol. 11, issue 10, 1-10

Abstract: 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.

Keywords: maximum power point tracker; solar irradiance classification system; extreme learning machine; support vector machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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