Journal of Renewable Energy and Environment

Quarterly Publication

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Publication Ethics

Indexing and Abstracting

Editorial Board

Journal Staff

Related Links

FAQ

Self-Archiving Policy

Paper Preparation Guide

Paper Template

Journal Forms

Reviewer

Peer Review Process

Optimizing Photovoltaic Arrays: A Novel Approach to Maximize Power Output in Varied Shading Patterns

Document Type : Research Article

Authors

1 Department of Electrical & Electronics Engineering,IIMT University, Meerut,250001,India

2 Department of R&D, IIMT University, Meerut, 250001,India

3 Department Of Electronics & Communication Engineering,IIMT College Of Engineering, Greater Noida,201306,India

10.30501/jree.2023.415011.1674

Abstract

The rapid rise in electrical energy demand and the depletion of fossil fuels have created a market for renewable energy. Among all the renewable energy resources, the most popular is solar energy, perceived as pollution-free, easily accessible, and low maintenance. In non-uniform solar irradiation or partial shading conditions (PSC), the photovoltaic characteristics (PVC) of a solar panel system (SPS) exhibit multiple minor peaks (MP) with one global peak power point (GPPP). To extract the utmost energy from the SPS, the authors proposed an efficient hybrid algorithm integrating the advantages of machine learning and the classical algorithm fractional open circuit voltage (FOVA) to track the GPPP. To follow the GPPP of SPS under unstable environmental surroundings, this study tests ML-based hybrid MPPT algorithms, specifically squared multiple variable linear regression algorithms (SMVLRA), using Matlab/Simulink. Simulation through Matlab is employed to validate the efficiency of the SMVLRA-MPPT approach compared to existing popular conventional and modern MPPT algorithms, namely the Perturb and Observation algorithm (P&OA), the variable step size incremental conductance (VINC) algorithm, and an intelligent algorithm, Decision Tree Regression Algorithm (DTRA). The simulation results demonstrate that SMVLRA offers higher peak power and mean peak power efficiency in less tracking time, with lower error and almost negligible steady-state fluctuation under PSC. The proposed algorithm achieves 99.99% efficiency under standard test conditions (1000w/m2, 25°C), 99.95% under PSC1 (1000w/m2, 800w/m2, 25°C), and 98.89% under PSC2 (1000w/m2, 800w/m2, 600w/m2, 25°C)
 

Keywords

Main Subjects

References
  1. A. Khaled, H. Aboubakeur, B. Mohamed and A. Nabil, 2018 "A Fast MPPT Control Technique Using PID Controller in a Photovoltaic System," 2018 International Conference on Applied Smart Systems (ICASS), Medea, Algeria, 2018, pp. 1-5, DOI: 10.1109/ICASS.2018.8652062
  2. Ahmad R, Murtaza AF, SherHA 2019 .P‘Power tracking techniques for efficient operation of photovoltaic array in solar applications.Renew Sustain Energy Rev. Mar 2019;101:82-102. https://doi.org/10.1016/j.rser.2018.10.015.
  3. Ali Reza Reisi , Mohammad Hassan Moradi , Shahriar Jamasb, March 2013 Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review, Renewable and Sustainable Energy Reviews Volume 19, March 2013, Pages 433-443, https://doi.org/10.1016/j.rser.2012.11.052.
  4. Behera MK, Majumder I, Nayak N. 2018 Solar photovoltaic power forecasting using optimized modified extreme learning machine technique. Engineering Science and Technology an International Journal’. Jun 2018;21(3):428-38. https://doi.org/10.1016/j.jestch.2018.04.013.
  5. Bendib B, Belmili H, Krim F, 2015. A survey of the most used MPPT methods: conventional and advanced algorithms applied for photovoltaic systems. Renew Sustain Energy Rev. 2015;45:637-48. https://doi.org/10.1016/j.rser.2015.02.009. 
  6. DuY, YanK, RenZ, XiaoW, 2018. Designing localized MPPT for PV systems using fuzzy-weighted extreme learning machine.Energies.2018;11(10):2615. https://doi.org/10.3390/en11102615
  7. E. Lorenzo, 1994, “Solar Electricity: Engineering of Photovoltaic Systems,” Chap. 3, Progensa, Earthscan Publications Ltd., Spain, 1994.
  8. Ethem Alpaydin 2014 Introduction to Machine Learning  MIT press. https://dl.matlabyar.com/siavash/ML/Book/Ethem%20Alpaydin-Introduction%20to%20Machine%20Learning-The%20MIT%20Press%20(2014).pdf
  9. Harrison, Ambe; Henry Alombah, Njimboh (2022). Solar PV Data: (a-m) Piecewise segmentation of the I-V curve. figshare. Dataset. https://doi.org/10.6084/m9.figshare.21677555.v1 
  10. Hegazy Rezk, Ahmed Fathy, September 2017” Simulation of global MPPT based on teaching–learning-based optimization technique for partially shaded PV system”,Springer, September 2017.Electrical Engineering 99(3) https://doi.org/10.1007/s00202-016-0449-3
  11. Hill, J.S.: ‘ Global solar market demand expected to reach 100 Gigawatts in 2017, says solarpower Europe’, 2017. Available at https://cleantechnica.com/2017/10/27/global-solar-market-demand-expected-reach-100-gw-2017-solarpower-europe/, accessed 05 November 2017.
  12. International Energy Agency: ‘ Snapshot of global photovoltaic markets 2017’, 2017. Available at http://www.ieapvps.org/fileadmin/dam/public/report/statistics/IEA-PVPS-A Snapshot of Global P V-1992-2016-1.pdf, accessed 05 November.
  13. Introduction to machine Learning ,The Wikipedia Guide,https://www.academia.edu/41157657/Introduction_to_Machine_Learning_The _wikipedia_Guide.
  14. Ishrat Zaiba, Yadav DK, Dr. Sharma DK, Dr. Nayak S. Review on:Challenges Solution & Scope Of Iot In Solar Energy. J East China Univ Sci Technol. 2022;65(4):587-94 http://hdlgdxxb.info/index.php/JE_CUST/article/view/477.
  15. Jordehi, A.R.: ‘Maximum power point tracking in photovoltaic (PV) systems, 2016” a review of different approaches’, Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1127-1138., DOI: 10.1016/j.rser.2016.07.053.
  16. Kalaiarasi Nallathambi Sanjeev kumar Padmanaban Sathesh Paramasivam 2018,” Maximum Power Point Tracking Implementation by Dspace Controller Integrated Through Z-Source Inverter Using Particle Swarm Optimization Technique for Photovoltaic Applications January 2018 Applied Sciences  DOI:10.3390/app8010145.
  17. Kalogerakis C, Koutroulis E, Lagoudakis MG, 2022,” Global MPPT Based on Machine-Learning for PV Arrays Operating under Partial Shading Conditions. Appl Sci;10(2). https://doi.org/10.3390/app10020700
  18. Kumar N, Hussain I, Singh B, Panigrahi BK, October 2017,” Framework of Maximum Power Extraction from Solar PV Panel Using Self Predictive Perturb and Observe Algorithm. IEEE Trans Sustain Energy 2018;9:895–903. https://doi.org/10.1109/TSTE.2017.2764266
  19. Kumar R, Singh SK 2018. Solar photovoltaic modeling and simulation: as a renewable energy solution. Energy Reports, 2018,4:701–712. https://doi.org/10.1016/j.egyr.2018.09.008
  20. M.H Rashid.Power Electronics: Devices, Circuits, and Applications Mahdi Rajabi Vincheh, Abbas Kargar &   Gholamreza Arab Markadeh, 2014,” A Hybrid Control Method for Maximum Power Point Tracking (MPPT) in Photovoltaic Systems”, Arabian Journal for Science and Engineering volume 39, pages4715–4725(2014), https://doi.org/DOI:10.1007/s13369-014-1056-0
  21. Mahesh Venketsha,Meyyappan S,Alla Rao,2022" Maximum power point tracking using decision-tree machine-learning algorithm for photovoltaic systems" Clean Energy, Volume 6, Issue 5, October 2022, Pages 762–775, https://doi.org/10.1093/ce/zkac057.
  22. Motahir S, El Hammoumi A, El Ghzizal A 2020. “The most used MPPT algorithms: Review and the suitable low-   cost embedded board for each algorithm. J Clean Prod 2020;246:118983. https://doi.org/10.1016/j.jclepro.2019.118983
  23. MounilMemayaa, C. BalakrishnaMoorthyb, SahityaTahilianic, SiddarthSreenid,”Machine learning based maximum power point tracking in solar energy conversion systems”, International Journal of Smart Grid and Clean Energy”, https://www.ijsgce.com/uploadfile/2019/0910/20190910030832865.pdf.
  24. N. Priyadarshi, F. Azam, A. K. Sharma, and M. Vardia, 2020, “An adaptive neuro- fuzzy inference system-based intelligent grid-connected photo-voltaic power generation,” in Advances in Computational Intelligence,S. K. Sahana and V. Bhattacharjee, Eds. Singapore: Springer, 2020, pp.3–14. January 2020 DOI:10.1007/978-981-13-8222-2_1
  25. Nkambule MS, Hasan AN, Ali A, HongJ, GeemZW 2021. Comprehensive evaluation of machinelearning MPPT algorithms for a PV system under different weather conditions. JElectr Eng Technol.2021;16(1):411-27. http://dx.doi.org/10.1007/s42835-020-00598-0 .
  26. Nugraha D A Lian KL, Suwarno 2019. A Novel MPPT Method Based on Cuckoo Search Algorithm and Golden Section Search Algorithm for Partially Shaded PV System’.CanJElectrComputEng. 2019;42(3):173- 82. https://doi.org/10.1109/CJECE.2019.2914723
  27. Owusu-Nyarko, I.; Elgenedy, M.A.; Abdelsalam, I.; Ahmed, K.H. 2021 Modified Variable Step-Size Incremental Conductance MPPT Technique for Photovoltaic Systems. Electronics 2021, 10, 2331. https://doi.org/10.3390/electronics10192331
  28. P.Venkata Mahesh,S. Meyyappan and Rama Koteswara 2022 , “Maximum power point tracking with regression machine-learning   algorithm for solar PV systems”,International Journal of Renewable  Energy Research,Vol -12  page no-3 sep 2022.https://doi.org/10.20508/ijrer.v12i3.13249.g8517
  29.  Podder AK, Roy NK, Pota HR 2019. MPPT methods for solar PV systems: a critical review based on tracking nature. IET Renew Power Gener. Jul 2019;13(10):1615-32. https://doi.org/10.1049/iet-rpg.2018.5946
  30. Rabaia MKH, Abdelkareem MA, Sayed ET, Elsaid K, Chae KJ, Wilberforce T et al.2021. Environmental impacts of solar energy systems: a review. Sci Total Environ. 2021;754:141989. https://doi.org/10.1016/j.scitotenv.2020.141989 ,PMID 32920388.
  31. Radjai T, Rahmani L, Mekhilef S, Gaubert JP 2014. “Implementation of a modified incremental conductance MPPT algorithm with direct control based on a fuzzy duty cycle change estimator using d-SPACE”. Sol Energy. Dec 2014;110:325-37. https://doi.org/10.1016/j.solener.2014.09.014
  32. Rafeeq AhmedK, SayeedF, LogavaniK, CatherineTJ, RalhanS, SinghM, et al 2022. Maximum power point tracking of PV grids using deep learning. IntJPhotoenergy.2022;2022:1-7.  https://doi.org/10.1016/j.solener.2014.09.014
  33. RakeshKumar Phanden , Lalit Sharma , Jatinder Chhabra , Halil İbrahim Demir 2020, A novel modified ant colony optimization based maximum power point tracking controller for photovoltaic systems” ,Material Today Proceeding, https://doi.org/10.1016/j.matpr.2020.06.020.
  34. Ruhi Sharmin SS, fayet Chowdhury FA, Mujibur Rahman Kazi, 2021” Implementation of MPPT Technique of Solar Module with Supervised Machine Learning”,Oct 2021. https://doi.org/10.3389/fenrg.2022.932653.
  35. Senthamizh Selvan Sakthivel & Venkadesan Arunachalam 2022 “Artificial Neural Network Assisted P &O-Based MPPT Controller for a Partially Shaded Grid-Connected Solar PV Panel”, Arabian Journal for Science and Engineering (2022), https://doi.org/10.1007/s13369-022-07566-y
  36. Seyed mahmoudian M, Horan B, SoonT K, Rahmani R, Than Oo AM, Mekhilef S et al 2016. State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems – a review.RenewSustain Energy Rev. Oct 2016;64:435-55. https://doi.org/10.1016/j.solener.2014.09.014
  37. Shaiek MB, Smida AS, Mimouni MF (2013) Comparison between conventional methods and {GA} approach for maximum power point tracking of shaded solar{PV}generators. Solar Energy 90:107122. https://www.sciencedirect.com/science/article/pii/S0038092X1300009.
  38. TakruriM, FarhatM, BarambonesO, Ramos-HernanzJA, TurkiehMJ, BadawiMet al 2020,”.Maximum power point tracking of PV system based on machine learning. Energies.2020;13(3):692. https://doi.org/10.3390/en13030692
  39. Verma D., Nema, S., Shandilya, A.M., et al 2016: ‘Maximum power point tracking (MPPT) techniques: recapitulation in solar photovoltaic systems’, Renew. Sustain. Energy Rev., 2016, 54, pp. 1018–1034. https://doi.org/10.1016/j.rser.2015.10.068
  40. Wafa hayder,Emanulle Ogliari, Alberto Dolara,2020, “Improved PSO: A Comparative Study in MPPT           Algorithm for PV System Control under Partial Shading Conditions” Energies 2020, 13(8), 2035; https://doi.org/10.3390/en13082035
  41. Yung Yap KY, SarimuthuCR, Mun-Yee LimJ. 2020,” Artificial Intelligence Based MPPT Techniques for Solar Power System: a review.JMod Power Syst Clean Energy. 2020;8(6, Nov):1043-59. https://doi.org/10.35833/MPCE.2020.000159
  42. Zaiba Ishrat,Ankur Kumar Gupta,Seema Nayak 2023, A Comprehensive Review of MPPT Techniques Based On ML Applicable for Maximum Power in Solar Power Systems,Journal Of Renenwable Energy and Environment, 10.30501/jree.2023.385661.1556

 

Journal of Renewable Energy and Environment
Volume 11, Issue 2
April 2024
Files
History
  • Receive Date: 14 September 2023
  • Revise Date: 23 October 2023
  • Accept Date: 26 November 2023
Share
How to cite
Statistics