TY - JOUR
T1 - Designing localized MPPT for PV systems using fuzzy-weighted extreme learning machine
AU - Du, Yang
AU - Yan, Ke
AU - Ren, Zixiao
AU - Xiao, Weidong
N1 - Publisher Copyright:
© 2018 by the authors.
PY - 2018/10
Y1 - 2018/10
N2 - 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.
AB - 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.
KW - Extreme learning machine
KW - Maximum power point tracker
KW - Solar irradiance classification system
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85056078534&partnerID=8YFLogxK
U2 - 10.3390/en11102615
DO - 10.3390/en11102615
M3 - Article
AN - SCOPUS:85056078534
VL - 11
JO - Energies
JF - Energies
IS - 10
M1 - 2615
ER -