IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i2p335-d477465.html
   My bibliography  Save this article

Maximum Power Point Tracking Based on Reinforcement Learning Using Evolutionary Optimization Algorithms

Author

Listed:
  • Kostas Bavarinos

    (Industrial Design and Production Engineering, University of West Attica, 250 Thivon & P. Ralli Str, 12241 Egaleo, Greece)

  • Anastasios Dounis

    (Biomedical Engineering, University of West Attica, Ag. Spyridonos 17, 12243 Egaleo, Greece)

  • Panagiotis Kofinas

    (Industrial Design and Production Engineering, University of West Attica, 250 Thivon & P. Ralli Str, 12241 Egaleo, Greece
    Biomedical Engineering, University of West Attica, Ag. Spyridonos 17, 12243 Egaleo, Greece)

Abstract

In this paper, two universal reinforcement learning methods are considered to solve the problem of maximum power point tracking for photovoltaics. Both methods exhibit fast achievement of the MPP under varying environmental conditions and are applicable in different PV systems. The only required knowledge of the PV system are the open-circuit voltage, the short-circuit current and the maximum power, all under STC, which are always provided by the manufacturer. Both methods are compared to a Fuzzy Logic Controller and the universality of the proposed methods is highlighted. After the implementation and the validation of proper performance of both methods, two evolutionary optimization algorithms (Big Bang—Big Crunch and Genetic Algorithm) are applied. The results demonstrate that both methods achieve higher energy production and in both methods the time for tracking the MPP is reduced, after the application of both evolutionary algorithms.

Suggested Citation

  • Kostas Bavarinos & Anastasios Dounis & Panagiotis Kofinas, 2021. "Maximum Power Point Tracking Based on Reinforcement Learning Using Evolutionary Optimization Algorithms," Energies, MDPI, vol. 14(2), pages 1-23, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:335-:d:477465
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/2/335/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/2/335/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Noureddine Bouarroudj & Djamel Boukhetala & Vicente Feliu-Batlle & Fares Boudjema & Boualam Benlahbib & Bachir Batoun, 2019. "Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System," Energies, MDPI, vol. 12(14), pages 1-19, July.
    2. Maissa Farhat & Oscar Barambones & Lassaâd Sbita, 2020. "A Real-Time Implementation of Novel and Stable Variable Step Size MPPT," Energies, MDPI, vol. 13(18), pages 1-18, September.
    3. John Macaulay & Zhongfu Zhou, 2018. "A Fuzzy Logical-Based Variable Step Size P&O MPPT Algorithm for Photovoltaic System," Energies, MDPI, vol. 11(6), pages 1-15, May.
    4. Tehzeeb-ul Hassan & Rabeh Abbassi & Houssem Jerbi & Kashif Mehmood & Muhammad Faizan Tahir & Khalid Mehmood Cheema & Rajvikram Madurai Elavarasan & Farman Ali & Irfan Ahmad Khan, 2020. "A Novel Algorithm for MPPT of an Isolated PV System Using Push Pull Converter with Fuzzy Logic Controller," Energies, MDPI, vol. 13(15), pages 1-20, August.
    5. Kofinas, P. & Doltsinis, S. & Dounis, A.I. & Vouros, G.A., 2017. "A reinforcement learning approach for MPPT control method of photovoltaic sources," Renewable Energy, Elsevier, vol. 108(C), pages 461-473.
    6. Carlos Robles Algarín & John Taborda Giraldo & Omar Rodríguez Álvarez, 2017. "Fuzzy Logic Based MPPT Controller for a PV System," Energies, MDPI, vol. 10(12), pages 1-18, December.
    7. Slimane Hadji & Jean-Paul Gaubert & Fateh Krim, 2018. "Real-Time Genetic Algorithms-Based MPPT: Study and Comparison (Theoretical an Experimental) with Conventional Methods," Energies, MDPI, vol. 11(2), pages 1-17, February.
    8. Chun-Liang Liu & Jing-Hsiao Chen & Yi-Hua Liu & Zong-Zhen Yang, 2014. "An Asymmetrical Fuzzy-Logic-Control-Based MPPT Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 7(4), pages 1-17, April.
    9. O. Fatih Kececioglu & Ahmet Gani & Mustafa Sekkeli, 2020. "Design and Hardware Implementation Based on Hybrid Structure for MPPT of PV System Using an Interval Type-2 TSK Fuzzy Logic Controller," Energies, MDPI, vol. 13(7), pages 1-18, April.
    10. Adeel Feroz Mirza & Majad Mansoor & Qiang Ling & Muhammad Imran Khan & Omar M. Aldossary, 2020. "Advanced Variable Step Size Incremental Conductance MPPT for a Standalone PV System Utilizing a GA-Tuned PID Controller," Energies, MDPI, vol. 13(16), pages 1-25, August.
    11. Dounis, Anastasios I. & Kofinas, Panagiotis & Alafodimos, Constantine & Tseles, Dimitrios, 2013. "Adaptive fuzzy gain scheduling PID controller for maximum power point tracking of photovoltaic system," Renewable Energy, Elsevier, vol. 60(C), pages 202-214.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fateh Mehazzem & Maina André & Rudy Calif, 2022. "Efficient Output Photovoltaic Power Prediction Based on MPPT Fuzzy Logic Technique and Solar Spatio-Temporal Forecasting Approach in a Tropical Insular Region," Energies, MDPI, vol. 15(22), pages 1-21, November.
    2. Eneko Artetxe & Jokin Uralde & Oscar Barambones & Isidro Calvo & Imanol Martin, 2023. "Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin," Mathematics, MDPI, vol. 11(9), pages 1-21, May.
    3. Noureddine Bouarroudj & Yehya Houam & Abdelhamid Djari & Vicente Feliu-Batlle & Abdelkader Lakhdari & Boualam Benlahbib, 2023. "A Linear Quadratic Integral Controller for PV-Module Voltage Regulation for the Purpose of Enhancing the Classical Incremental Conductance Algorithm," Energies, MDPI, vol. 16(11), pages 1-17, June.
    4. Amit Kumar Sharma & Rupendra Kumar Pachauri & Sushabhan Choudhury & Ahmad Faiz Minai & Majed A. Alotaibi & Hasmat Malik & Fausto Pedro García Márquez, 2023. "Role of Metaheuristic Approaches for Implementation of Integrated MPPT-PV Systems: A Comprehensive Study," Mathematics, MDPI, vol. 11(2), pages 1-48, January.
    5. Sy Ngo & Chian-Song Chiu & Thanh-Dong Ngo, 2022. "A Novel Horse Racing Algorithm Based MPPT Control for Standalone PV Power Systems," Energies, MDPI, vol. 15(20), pages 1-18, October.
    6. Neeraj Priyadarshi & Vigna K. Ramachandaramurthy & Sanjeevikumar Padmanaban & Farooque Azam, 2019. "An Ant Colony Optimized MPPT for Standalone Hybrid PV-Wind Power System with Single Cuk Converter," Energies, MDPI, vol. 12(1), pages 1-23, January.
    7. Esteban Guerrero-Ramirez & Alberto Martinez-Barbosa & Marco Antonio Contreras-Ordaz & Gerardo Guerrero-Ramirez & Enrique Guzman-Ramirez & Jorge Luis Barahona-Avalos & Manuel Adam-Medina, 2022. "DC Motor Drive Powered by Solar Photovoltaic Energy: An FPGA-Based Active Disturbance Rejection Control Approach," Energies, MDPI, vol. 15(18), pages 1-36, September.
    8. Tehzeeb-ul Hassan & Rabeh Abbassi & Houssem Jerbi & Kashif Mehmood & Muhammad Faizan Tahir & Khalid Mehmood Cheema & Rajvikram Madurai Elavarasan & Farman Ali & Irfan Ahmad Khan, 2020. "A Novel Algorithm for MPPT of an Isolated PV System Using Push Pull Converter with Fuzzy Logic Controller," Energies, MDPI, vol. 13(15), pages 1-20, August.
    9. Mohamed Derbeli & Cristian Napole & Oscar Barambones, 2023. "A Fuzzy Logic Control for Maximum Power Point Tracking Algorithm Validated in a Commercial PV System," Energies, MDPI, vol. 16(2), pages 1-14, January.
    10. Khaled Bataineh & Naser Eid, 2018. "A Hybrid Maximum Power Point Tracking Method for Photovoltaic Systems for Dynamic Weather Conditions," Resources, MDPI, vol. 7(4), pages 1-16, November.
    11. Moayed Almobaied & Hassan S. Al-Nahhal & Orlando Arrieta & Ramon Vilanova, 2023. "Design a Robust Proportional-Derivative Gain-Scheduling Control for a Magnetic Levitation System," Mathematics, MDPI, vol. 11(19), pages 1-21, September.
    12. Camilo, Jones C. & Guedes, Tatiana & Fernandes, Darlan A. & Melo, J.D. & Costa, F.F. & Sguarezi Filho, Alfeu J., 2019. "A maximum power point tracking for photovoltaic systems based on Monod equation," Renewable Energy, Elsevier, vol. 130(C), pages 428-438.
    13. Mohamed Derbeli & Asma Charaabi & Oscar Barambones & Cristian Napole, 2021. "High-Performance Tracking for Proton Exchange Membrane Fuel Cell System PEMFC Using Model Predictive Control," Mathematics, MDPI, vol. 9(11), pages 1-17, May.
    14. Haoming Liu & Muhammad Yasir Ali Khan & Xiaoling Yuan, 2023. "Hybrid Maximum Power Extraction Methods for Photovoltaic Systems: A Comprehensive Review," Energies, MDPI, vol. 16(15), pages 1-64, July.
    15. Diego R. Espinoza Trejo & Ernesto Bárcenas & José E. Hernández Díez & Guillermo Bossio & Gerardo Espinosa Pérez, 2018. "Open- and Short-Circuit Fault Identification for a Boost dc/dc Converter in PV MPPT Systems," Energies, MDPI, vol. 11(3), pages 1-15, March.
    16. Triet Nguyen-Van & Rikiya Abe & Kenji Tanaka, 2018. "MPPT and SPPT Control for PV-Connected Inverters Using Digital Adaptive Hysteresis Current Control," Energies, MDPI, vol. 11(8), pages 1-16, August.
    17. Emad M. Ahmed & Mokhtar Aly & Ahmed Elmelegi & Abdullah G. Alharbi & Ziad M. Ali, 2019. "Multifunctional Distributed MPPT Controller for 3P4W Grid-Connected PV Systems in Distribution Network with Unbalanced Loads," Energies, MDPI, vol. 12(24), pages 1-19, December.
    18. Guerrero-Rodríguez, N.F. & Rey-Boué, Alexis B. & Herrero-de Lucas, Luis C. & Martinez-Rodrigo, Fernando, 2015. "Control and synchronization algorithms for a grid-connected photovoltaic system under harmonic distortions, frequency variations and unbalances," Renewable Energy, Elsevier, vol. 80(C), pages 380-395.
    19. Chettibi, N. & Mellit, A., 2018. "Intelligent control strategy for a grid connected PV/SOFC/BESS energy generation system," Energy, Elsevier, vol. 147(C), pages 239-262.
    20. Abdelbasset Krama & Laid Zellouma & Boualaga Rabhi & Shady S. Refaat & Mansour Bouzidi, 2018. "Real-Time Implementation of High Performance Control Scheme for Grid-Tied PV System for Power Quality Enhancement Based on MPPC-SVM Optimized by PSO Algorithm," Energies, MDPI, vol. 11(12), pages 1-26, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:335-:d:477465. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.