Maximum Power Point Tracking of PV Grids Using Deep Learning

Author:

Rafeeq Ahmed K.1,Sayeed Farrukh2,Logavani K.3,Catherine T. J.4,Ralhan Shimpy5,Singh Mahesh5,Prabu R. Thandaiah6,Subramanian B. Bala7,Kassa Adane8ORCID

Affiliation:

1. Department of Electronics and Communication Engineering, School of Engineering, Presidency University, Bangalore, 560064, India

2. Department of Electrical and Electronics Engineering, ACE College of Engineering, Karinkadamugal, Thiruvananthapuram, Kerala 695027, India

3. Department of Electrical and Electronics Engineering, Government College of Engineering, Salem, Tamil Nadu 636011, India

4. Department of Electrical and Electronics Engineering, R.M.K. College of Engineering and Technology, Puduvoyal, Thiruvallur, Tamil Nadu 601206, India

5. Department of Electrical and Electronics Engineering, Shri Shankaracharya Technical Campus, Durg, Bhilai, Chhattisgarh 490020, India

6. Department of Electronics and Communication Engineering, Saveetha School of Engineering, SIMATS, Chennai, 602105 Tamil Nadu, India

7. Department of Biotechnology, Sejong University, Seoul, Republic of Korea

8. Faculty of Mechanical Engineering, Arba Minch Institute of Technology (AMIT), Arba Minch University, Ethiopia

Abstract

In this paper, we develop a deep learning model using back propagation neural network (BPNN) that helps to obtain maximum power point. This deep learning model aims to maximise the output power from the solar grids when the panels are connected with the boost converter under different variable load conditions. BPNN-DL enables the prediction of reference voltage at different weather conditions for severing the various output power that ensures maximum power with stable output voltage. The proposed BPNN-DL is tested under different conditions to estimate the robustness of the modules under internal/external interferences. The results of the simulation show that the proposed method achieves maximum output power from each panel compared with existing methods in terms of regression analysis on training, testing, and validation.

Funder

Aditya Engineering College

Publisher

Hindawi Limited

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,Atomic and Molecular Physics, and Optics,General Chemistry

Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. AI-Driven Test and Measurement Automation in Electronics Manufacturing;2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM);2024-04-04

2. Neural Network-Based Load Forecasting for Power Grids;2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM);2024-04-04

3. Deep Learning Methods for Detecting ImageBased Defects in Manufacturing Processes;2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM);2024-04-04

4. Enhance quality of Power in Grid Tie Solar Photovoltaic System using Deep learning MPPT;2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE);2024-02-22

5. Fuzzy logic inherited machine learning based maximum power point tracker for cost-optimized grid connected hybrid renewable systems;IRAN J FUZZY SYST;2024

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3