Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)

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Maximum Power Point Tracking Control Based on Variable Step Size Perturbation Observation Method

Authors
Chao Ma1, Shengguo Zhang1, *, Hao Hou2, Zihao Wang2, Changxun Yu2
1Institute of Problem Solving, Northwest Minzu University, Gansu, Lanzhou, China
2Department of modeling, Northwest Minzu University, Gansu, Lanzhou, China
*Corresponding author. Email: bzhangshengguo@tsinghua.org.cn
Corresponding Author
Shengguo Zhang
Available Online 27 December 2022.
DOI
10.2991/978-94-6463-040-4_35How to use a DOI?
Keywords
Maximum Power Point Tracking; Variable Step Size Perturbation Observation; Hybrid PV-Battery System
Abstract

In the whole life cycle of the evaluation, construction, grid connection, operation and maintenance and sale of power plant projects, the calculation of power generation with artificial intelligence technology as the core is the top priority. Maximum power points tracking techniques are widely utilized in photovoltaic systems to operate at the peak power of PV array which depends on solar and ambient temperature. the perturbation observation method algorithm is the most applied maximum power point tracking control scheme in photovoltaic applications for its simplicity and ease of implementation. But conventional perturbation and observation method suffers from steady state oscillations. To avoid this, this paper emphasizes on a variable step perturbation and observation which can able to track the maximum power point rapidly with less steady state oscillation as compared with conventional perturbation and observation algorithm. The efficacy of the proposed method is verified considering both simulation and experimental results by modelling.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)
Series
Atlantis Highlights in Computer Sciences
Publication Date
27 December 2022
ISBN
10.2991/978-94-6463-040-4_35
ISSN
2589-4900
DOI
10.2991/978-94-6463-040-4_35How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Chao Ma
AU  - Shengguo Zhang
AU  - Hao Hou
AU  - Zihao Wang
AU  - Changxun Yu
PY  - 2022
DA  - 2022/12/27
TI  - Maximum Power Point Tracking Control Based on Variable Step Size Perturbation Observation Method
BT  - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)
PB  - Atlantis Press
SP  - 233
EP  - 237
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-040-4_35
DO  - 10.2991/978-94-6463-040-4_35
ID  - Ma2022
ER  -