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

doi:10.1007/978-981-10-1536-6_52

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 proceeding › Conference Proceeding › peer-review

Abstract

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 language	English
Title of host publication	Advanced Multimedia and Ubiquitous Engineering - FutureTech and MUE
Editors	Hai Jin, Young-Sik Jeong, Muhammad Khurram Khan, James J. Park
Publisher	Springer Verlag
Pages	397-404
Number of pages	8
ISBN (Print)	9789811015359
DOIs	https://doi.org/10.1007/978-981-10-1536-6_52
Publication status	Published - 2016
Event	11th International Conference on Future Information Technology, FutureTech 2016 - Beijing, China Duration: 20 Apr 2016 → 22 Apr 2016

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	393
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	11th International Conference on Future Information Technology, FutureTech 2016
Country/Territory	China
City	Beijing
Period	20/04/16 → 22/04/16

Keywords

Maximum power point estimation
PV modules
RBFNN

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-981-10-1536-6_52

Cite this

Ma, J., Bi, Z., Jiang, Y., Tian, X., Zhang, Y., Lim, E. G., & Man, K. L. (2016). Maximum power point estimation for photovoltaic modules via RBFNN. In H. Jin, Y.-S. Jeong, M. K. Khan, & J. J. Park (Eds.), Advanced Multimedia and Ubiquitous Engineering - FutureTech and MUE (pp. 397-404). (Lecture Notes in Electrical Engineering; Vol. 393). Springer Verlag. https://doi.org/10.1007/978-981-10-1536-6_52

@inproceedings{f2bdaed7ea9549c68c0df37b908ee344,

title = "Maximum power point estimation for photovoltaic modules via RBFNN",

abstract = "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.",

keywords = "Maximum power point estimation, PV modules, RBFNN",

author = "Jieming Ma and Ziqiang Bi and Yue Jiang and Xiangyu Tian and Yungang Zhang and Lim, {Eng Gee} and Man, {Ka Lok}",

note = "Publisher Copyright: {\textcopyright} Springer Science+Business Media Singapore 2016.; 11th International Conference on Future Information Technology, FutureTech 2016 ; Conference date: 20-04-2016 Through 22-04-2016",

year = "2016",

doi = "10.1007/978-981-10-1536-6_52",

language = "English",

isbn = "9789811015359",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Verlag",

pages = "397--404",

editor = "Hai Jin and Young-Sik Jeong and Khan, {Muhammad Khurram} and Park, {James J.}",

booktitle = "Advanced Multimedia and Ubiquitous Engineering - FutureTech and MUE",

}

Ma, J, Bi, Z, Jiang, Y, Tian, X, Zhang, Y, Lim, EG & Man, KL 2016, Maximum power point estimation for photovoltaic modules via RBFNN. in H Jin, Y-S Jeong, MK Khan & JJ Park (eds), Advanced Multimedia and Ubiquitous Engineering - FutureTech and MUE. Lecture Notes in Electrical Engineering, vol. 393, Springer Verlag, pp. 397-404, 11th International Conference on Future Information Technology, FutureTech 2016, Beijing, China, 20/04/16. https://doi.org/10.1007/978-981-10-1536-6_52

Maximum power point estimation for photovoltaic modules via RBFNN. / Ma, Jieming; Bi, Ziqiang; Jiang, Yue et al.
Advanced Multimedia and Ubiquitous Engineering - FutureTech and MUE. ed. / Hai Jin; Young-Sik Jeong; Muhammad Khurram Khan; James J. Park. Springer Verlag, 2016. p. 397-404 (Lecture Notes in Electrical Engineering; Vol. 393).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Maximum power point estimation for photovoltaic modules via RBFNN

AU - Ma, Jieming

AU - Bi, Ziqiang

AU - Jiang, Yue

AU - Tian, Xiangyu

AU - Zhang, Yungang

AU - Lim, Eng Gee

AU - Man, Ka Lok

N1 - Publisher Copyright: © Springer Science+Business Media Singapore 2016.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Maximum power point estimation

KW - PV modules

KW - RBFNN

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U2 - 10.1007/978-981-10-1536-6_52

DO - 10.1007/978-981-10-1536-6_52

M3 - Conference Proceeding

AN - SCOPUS:84988027610

SN - 9789811015359

T3 - Lecture Notes in Electrical Engineering

SP - 397

EP - 404

BT - Advanced Multimedia and Ubiquitous Engineering - FutureTech and MUE

A2 - Jin, Hai

A2 - Jeong, Young-Sik

A2 - Khan, Muhammad Khurram

A2 - Park, James J.

PB - Springer Verlag

T2 - 11th International Conference on Future Information Technology, FutureTech 2016

Y2 - 20 April 2016 through 22 April 2016

ER -

Maximum power point estimation for photovoltaic modules via RBFNN

Abstract

Publication series

Conference

Keywords

UN SDGs

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