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

Yang Du, Ke Yan*, Zixiao Ren, Weidong Xiao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)


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.

Original languageEnglish
Article number2615
Issue number10
Publication statusPublished - Oct 2018


  • Extreme learning machine
  • Maximum power point tracker
  • Solar irradiance classification system
  • Support vector machine


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