Photovoltaic Maximum Power Point Tracking Control and Neural Network Modeling Based on Improved Perturbation Observation Method-Reference-Cited by-同舟云学术

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Photovoltaic Maximum Power Point Tracking Control and Neural Network Modeling Based on Improved Perturbation Observation Method

Published:2023-07-14 Issue: Volume: Page:
ISSN:
Container-title:2023 6th International Conference on Signal Processing and Machine Learning (SPML)
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Author:

Wang Chenlong¹^ORCID,Ouyang Tianyu¹^ORCID,Ma Fengying¹^ORCID,Ji Peng¹^ORCID,Wu Liming¹^ORCID,Wang Fengying²^ORCID,Mu Gaofeng²^ORCID

Affiliation:

1. School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), China

2. Shandong Mingda Electric Appliance Co., LTD, China

Funder

National Natural Science Foundation Program

Key R&D plan of Shandong Province (major scientific and technological innovation project)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3614008.3614049

Reference10 articles.

1. A comprehensive review on solar PV maximum power point tracking techniques

2. A Time-Based Global Maximum Power Point Tracking Technique for PV System

3. Maximum power point tracking strategy for photovoltaic system based on fuzzy information diffusion under partial shading conditions

4. Hybrid Maximum Power Point Tracking Technique for PV Modules Based on a Double-Diode Model

5. Maximum power point tracking and optimal Li-ion battery charging control for photovoltaic charging system

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