A Novel Comparative Analysis of Solar P&O, ANN-based MPPT Controller under Different Irradiance Condition
DOI:
https://doi.org/10.4108/ew.4942Keywords:
PV system, P&O, ANN, MPPTAbstract
The depletion of fossil fuels and rising energy demand have increased the use of renewable energy. Among all Solar PVs, system-based electricity production is increased due to multiple advantages. In this paper a Solar PV system with an Artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) controller is developed. ANN has multiple advantages like stability, improved dynamic response, and fast and precise output. The System is modelled with a DC-DC boost converter with Perturb and Observe (P&O)-based MPPT controller which is operated in MATLAB-based Simulink model. Both the controller output is analyzed and compared, among these two controllers ANN has very fast and more precise output under dynamic conditions.
Downloads
References
Vijay K Sood, Haytham Abdel Gawad. Power converter solutions and controls for green energy. DERM. 2019; 357-387. DOI: https://doi.org/10.1016/B978-0-12-817774-7.00014-4
Zegaoui, Aillerie M, Petit P, Sawicki J P, Charles J P, Belarbi A W. Dynamic behaviour of PV engine converting energy trackers under dissemination and heat changes. SE. 2011; 85; 2953–2964. DOI: https://doi.org/10.1016/j.solener.2011.08.038
Babu W R, Pushpalatha N, Catherine L, Janani K, Kanase S S and Patil P. Review and Comparison on Types of Solar Tracking using PNT Systems. ICICCS. 2023; 1697-1701. DOI: https://doi.org/10.1109/ICICCS56967.2023.10142648
Pushpalatha N, Devi B P, Sharma V and Alkhayyat A. A Comprehensive Study of AI-based Optimal Potential Point Tracking for Solar PV Frameworks. GlobConET. 2023; 1-5. DOI: https://doi.org/10.1109/GlobConET56651.2023.10149990
Pushpalatha N, Jabeera S, Hemalatha N, Sharma V, Balusamy B and Yuvaraj R. A Succinct Summary of the Solar MPPT Utilizing a Diverse Optimizing Compiler. IC3I. 2022; 1177-1181. DOI: https://doi.org/10.1109/IC3I56241.2022.10072844
Dutta R and Gupta R P. Performance analysis of MPPT based PV system: A case study. ICEFEET, 2022; 1-6 DOI: https://doi.org/10.1109/ICEFEET51821.2022.9847729
Pavithra C, Pooja Singh B, Venkatesa Prabhu S. A brief overview of maximum power point tracking algorithm for solar PV system. MTP. 2021. DOI: https://doi.org/10.1016/j.matpr.2021.01.220
Kaushik C, Garg R and Priya Mahajan P. Comparison of MPPT Algorithms Under Uniformly Varying Atmospheric Conditions. ICICICT, 2022. DOI: https://doi.org/10.1109/ICICICT54557.2022.9917652
Pavithra C, Geethamani R. A Noval Improved Variable Step-Size Incremental Resistance MPPT Controller for PV System Under Partial Shading Condition. JCTN. 2019; 16; 740- 744. DOI: https://doi.org/10.1166/jctn.2019.7801
Saravanan S, Ramesh Babu N. RBFN-based MPPT algorithm for PV system with high step-up converter. Elsevier BV. 2016; 122; 239-251. DOI: https://doi.org/10.1016/j.enconman.2016.05.076
Manna S and Akella A K. Comparative analysis of various P & O MPPT algorithm for PV system under varying radiation condition. ICPEE. 2021; 1-7. DOI: https://doi.org/10.1109/ICPEE50452.2021.9358690
Pavithra C, Geethamani R, Radhakrishnan G, Kishore Kumar S, and Manoj C. A Novel Grid Integrated Perturb and Observe MPPT Controlled Photovoltaic Power Plant for Power Enhancement. JCTN. 2019; 16; 410-416. DOI: https://doi.org/10.1166/jctn.2019.7741
Bouakkaz M S, Boukadoum A, Boudebbouz O, Bouraiou A, Boutasseta N and Attoui I. ANN based MPPT Algorithm Design using Real Operating Climatic Condition. ICMIT. 2020; 1-8. DOI: https://doi.org/10.1109/ICMIT47780.2020.9046972
Roy R B, Rokonuzzaman M D, Amin N, Mishu M K, Alahakoon S, Rahman S. A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System. IEEE Access. 2021; 9; 102137-102152 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3096864
Ujjwal Kumar Kalla, Senior Member. A Normalized Neural Network Based Controller for Power Quality Improved Grid Connected Solar PV System. PIICON. 2016; 1-7.
Bhim Singh, Dilip Tekchand Shahani, Arun Kumar Verma. Neural network-controlled grid interfaced solar photovoltaic power generation. IET. 2014; 7(3); 614-626. DOI: https://doi.org/10.1049/iet-pel.2013.0166
Sathiya Bama S, Dr. K Punitha. Artificial Neural Network for Control and Grid Integration of Floating Solar Photovoltaic System. IJAREEIE. 2018; 7 (11).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 EAI Endorsed Transactions on Energy Web
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.