Maximum power point estimation for photovoltaic modules via RBFNN

Jieming Ma*, Ziqiang Bi, Yue Jiang, Xiangyu Tian, Yungang Zhang, Eng Gee Lim, Ka Lok Man

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


Quantitative information of maximum power point (MPP) is crucial for controlling and optimizing the output power of photovoltaic (PV) modules. However, it is difficult to obtain the voltage at MPP through direct measurements. A novel approach of radial basis function neural network (RBFNN) is proposed to achieve maximum power point estimation in this study. The proposed method has the capability of determining the MPP of PV arrays directly from the measured current–voltage data of PV modules, and takes advantages of no need of internal parameters of PV model. The experimental results show that the proposed approach can obtain the optimal power output in high accuracy.

Original languageEnglish
Title of host publicationAdvanced Multimedia and Ubiquitous Engineering - FutureTech and MUE
EditorsHai Jin, Young-Sik Jeong, Muhammad Khurram Khan, James J. Park
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9789811015359
Publication statusPublished - 2016
Event11th International Conference on Future Information Technology, FutureTech 2016 - Beijing, China
Duration: 20 Apr 201622 Apr 2016

Publication series

NameLecture Notes in Electrical Engineering
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


Conference11th International Conference on Future Information Technology, FutureTech 2016


  • Maximum power point estimation
  • PV modules

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