IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i6p5338-5360d50686.html
   My bibliography  Save this article

Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique

Author

Listed:
  • Po-Chen Cheng

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, EE-105-1 #No.43, Sec. 4, Keelung Rd., Da'an Dist., Taipei 10600, Taiwan
    These authors contributed equally to this work.)

  • Bo-Rei Peng

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, EE-105-1 #No.43, Sec. 4, Keelung Rd., Da'an Dist., Taipei 10600, Taiwan
    These authors contributed equally to this work.)

  • Yi-Hua Liu

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, EE-105-1 #No.43, Sec. 4, Keelung Rd., Da'an Dist., Taipei 10600, Taiwan)

  • Yu-Shan Cheng

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, EE-105-1 #No.43, Sec. 4, Keelung Rd., Da'an Dist., Taipei 10600, Taiwan)

  • Jia-Wei Huang

    (Electric Energy Technology Division Power Electronics Department, Industrial Technology Research Institute, Rm#839, Bldg. 51, No. 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu 31040, Taiwan)

Abstract

In this paper, an asymmetrical fuzzy-logic-control (FLC)-based maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems is presented. Two membership function (MF) design methodologies that can improve the effectiveness of the proposed asymmetrical FLC-based MPPT methods are then proposed. The first method can quickly determine the input MF setting values via the power–voltage (P–V) curve of solar cells under standard test conditions (STC). The second method uses the particle swarm optimization (PSO) technique to optimize the input MF setting values. Because the PSO approach must target and optimize a cost function, a cost function design methodology that meets the performance requirements of practical photovoltaic generation systems (PGSs) is also proposed. According to the simulated and experimental results, the proposed asymmetrical FLC-based MPPT method has the highest fitness value, therefore, it can successfully address the tracking speed/tracking accuracy dilemma compared with the traditional perturb and observe (P&O) and symmetrical FLC-based MPPT algorithms. Compared to the conventional FLC-based MPPT method, the obtained optimal asymmetrical FLC-based MPPT can improve the transient time and the MPPT tracking accuracy by 25.8% and 0.98% under STC, respectively.

Suggested Citation

  • Po-Chen Cheng & Bo-Rei Peng & Yi-Hua Liu & Yu-Shan Cheng & Jia-Wei Huang, 2015. "Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique," Energies, MDPI, vol. 8(6), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:6:p:5338-5360:d:50686
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/6/5338/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/8/6/5338/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chih-Lung Shen & Cheng-Tao Tsai, 2012. "Double-Linear Approximation Algorithm to Achieve Maximum-Power-Point Tracking for Photovoltaic Arrays," Energies, MDPI, vol. 5(6), pages 1-16, June.
    2. Gounden, N. Ammasai & Ann Peter, Sabitha & Nallandula, Himaja & Krithiga, S., 2009. "Fuzzy logic controller with MPPT using line-commutated inverter for three-phase grid-connected photovoltaic systems," Renewable Energy, Elsevier, vol. 34(3), pages 909-915.
    3. Her-Terng Yau & Chen-Han Wu, 2011. "Comparison of Extremum-Seeking Control Techniques for Maximum Power Point Tracking in Photovoltaic Systems," Energies, MDPI, vol. 4(12), pages 1-16, December.
    4. Jaw-Kuen Shiau & Min-Yi Lee & Yu-Chen Wei & Bo-Chih Chen, 2014. "Circuit Simulation for Solar Power Maximum Power Point Tracking with Different Buck-Boost Converter Topologies," Energies, MDPI, vol. 7(8), pages 1-20, August.
    5. June-Seok Lee & Kyo Beum Lee, 2013. "Variable DC-Link Voltage Algorithm with a Wide Range of Maximum Power Point Tracking for a Two-String PV System," Energies, MDPI, vol. 6(1), pages 1-21, January.
    6. Altas, I.H. & Sharaf, A.M., 2008. "A novel maximum power fuzzy logic controller for photovoltaic solar energy systems," Renewable Energy, Elsevier, vol. 33(3), pages 388-399.
    7. Jaw-Kuen Shiau & Yu-Chen Wei & Min-Yi Lee, 2015. "Fuzzy Controller for a Voltage-Regulated Solar-Powered MPPT System for Hybrid Power System Applications," Energies, MDPI, vol. 8(5), pages 1-21, April.
    8. Syafaruddin, & Karatepe, Engin & Hiyama, Takashi, 2009. "Polar coordinated fuzzy controller based real-time maximum-power point control of photovoltaic system," Renewable Energy, Elsevier, vol. 34(12), pages 2597-2606.
    9. Larbes, C. & Aït Cheikh, S.M. & Obeidi, T. & Zerguerras, A., 2009. "Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system," Renewable Energy, Elsevier, vol. 34(10), pages 2093-2100.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nabipour, M. & Razaz, M. & Seifossadat, S.GH & Mortazavi, S.S., 2017. "A new MPPT scheme based on a novel fuzzy approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1147-1169.
    2. Hannan, M.A. & Lipu, M.S. Hossain & Ker, Pin Jern & Begum, R.A. & Agelidis, Vasilios G. & Blaabjerg, F., 2019. "Power electronics contribution to renewable energy conversion addressing emission reduction: Applications, issues, and recommendations," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Maher G. M. Abdolrasol & Mahammad Abdul Hannan & S. M. Suhail Hussain & Taha Selim Ustun & Mahidur R. Sarker & Pin Jern Ker, 2021. "Energy Management Scheduling for Microgrids in the Virtual Power Plant System Using Artificial Neural Networks," Energies, MDPI, vol. 14(20), pages 1-19, October.
    4. Doudou N. Luta & Atanda K. Raji, 2019. "Fuzzy Rule-Based and Particle Swarm Optimisation MPPT Techniques for a Fuel Cell Stack," Energies, MDPI, vol. 12(5), pages 1-15, March.
    5. Ammar Hussein Mutlag & Azah Mohamed & Hussain Shareef, 2016. "A Nature-Inspired Optimization-Based Optimum Fuzzy Logic Photovoltaic Inverter Controller Utilizing an eZdsp F28335 Board," Energies, MDPI, vol. 9(3), pages 1-32, February.
    6. Tanaselan Ramalu & Mohd Amran Mohd Radzi & Muhammad Ammirrul Atiqi Mohd Zainuri & Noor Izzri Abdul Wahab & Ribhan Zafira Abdul Rahman, 2016. "A Photovoltaic-Based SEPIC Converter with Dual-Fuzzy Maximum Power Point Tracking for Optimal Buck and Boost Operations," Energies, MDPI, vol. 9(8), pages 1-17, July.
    7. Hannan, M.A. & Ali, Jamal A. & Mohamed, Azah & Hussain, Aini, 2018. "Optimization techniques to enhance the performance of induction motor drives: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1611-1626.
    8. Giovanni Pau & Mario Collotta & Vincenzo Maniscalco, 2017. "Bluetooth 5 Energy Management through a Fuzzy-PSO Solution for Mobile Devices of Internet of Things," Energies, MDPI, vol. 10(7), pages 1-22, July.
    9. Gul Filiz Tchoketch Kebir & Cherif Larbes & Adrian Ilinca & Thameur Obeidi & Selma Tchoketch Kebir, 2018. "Study of the Intelligent Behavior of a Maximum Photovoltaic Energy Tracking Fuzzy Controller," Energies, MDPI, vol. 11(12), pages 1-20, November.
    10. Yilmaz, Unal & Kircay, Ali & Borekci, Selim, 2018. "PV system fuzzy logic MPPT method and PI control as a charge controller," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 994-1001.
    11. 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.
    12. Julio López Seguel & Seleme I. Seleme, 2021. "Robust Digital Control Strategy Based on Fuzzy Logic for a Solar Charger of VRLA Batteries," Energies, MDPI, vol. 14(4), pages 1-27, February.
    13. Musong L. Katche & Augustine B. Makokha & Siagi O. Zachary & Muyiwa S. Adaramola, 2023. "A Comprehensive Review of Maximum Power Point Tracking (MPPT) Techniques Used in Solar PV Systems," Energies, MDPI, vol. 16(5), pages 1-23, February.
    14. Jamal Abd Ali & Mahammad A Hannan & Azah Mohamed, 2015. "A Novel Quantum-Behaved Lightning Search Algorithm Approach to Improve the Fuzzy Logic Speed Controller for an Induction Motor Drive," Energies, MDPI, vol. 8(11), pages 1-25, November.
    15. 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.
    16. 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.
    17. Bijan Rahmani & Weixing Li, 2016. "Proposing Wavelet-Based Low-Pass Filter and Input Filter to Improve Transient Response of Grid-Connected Photovoltaic Systems," Energies, MDPI, vol. 9(8), pages 1-15, August.
    18. Chendi Li & Yuanrui Chen & Dongbao Zhou & Junfeng Liu & Jun Zeng, 2016. "A High-Performance Adaptive Incremental Conductance MPPT Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 9(4), pages 1-17, April.
    19. Syed Zulqadar Hassan & Hui Li & Tariq Kamal & Uğur Arifoğlu & Sidra Mumtaz & Laiq Khan, 2017. "Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 10(3), pages 1-16, March.
    20. Kermadi, Mostefa & Berkouk, El Madjid, 2017. "Artificial intelligence-based maximum power point tracking controllers for Photovoltaic systems: Comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 369-386.

    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. 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.
    2. Rajesh, R. & Carolin Mabel, M., 2015. "A comprehensive review of photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 231-248.
    3. Datta, Manoj & Senjyu, Tomonobu & Yona, Atsushi & Funabashi, Toshihisa, 2011. "A fuzzy based method for leveling output power fluctuations of photovoltaic-diesel hybrid power system," Renewable Energy, Elsevier, vol. 36(6), pages 1693-1703.
    4. Mellit, Adel & Kalogirou, Soteris A., 2014. "MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives," Energy, Elsevier, vol. 70(C), pages 1-21.
    5. Shahrooz Hajighorbani & Mohd Amran Mohd Radzi & Mohd Zainal Abidin Ab Kadir & Suhaidi Shafie, 2015. "Dual Search Maximum Power Point (DSMPP) Algorithm Based on Mathematical Analysis under Shaded Conditions," Energies, MDPI, vol. 8(10), pages 1-31, October.
    6. Jaw-Kuen Shiau & Yu-Chen Wei & Min-Yi Lee, 2015. "Fuzzy Controller for a Voltage-Regulated Solar-Powered MPPT System for Hybrid Power System Applications," Energies, MDPI, vol. 8(5), pages 1-21, April.
    7. Kakosimos, Panagiotis E. & Kladas, Antonios G., 2011. "Implementation of photovoltaic array MPPT through fixed step predictive control technique," Renewable Energy, Elsevier, vol. 36(9), pages 2508-2514.
    8. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    9. Danandeh, M.A. & Mousavi G., S.M., 2018. "Comparative and comprehensive review of maximum power point tracking methods for PV cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2743-2767.
    10. Shahrooz Hajighorbani & Mohd Amran Mohd Radzi & Mohd Zainal Abidin Ab Kadir & Suhaidi Shafie & Muhammad Ammirrul Atiqi Mohd Zainuri, 2016. "Implementing a Novel Hybrid Maximum Power Point Tracking Technique in DSP via Simulink/MATLAB under Partially Shaded Conditions," Energies, MDPI, vol. 9(2), pages 1-25, January.
    11. Rajesh, R. & Mabel, M. Carolin, 2016. "Design and real time implementation of a novel rule compressed fuzzy logic method for the determination operating point in a photo voltaic system," Energy, Elsevier, vol. 116(P1), pages 140-153.
    12. Andrés Tobón & Julián Peláez-Restrepo & Juan P. Villegas-Ceballos & Sergio Ignacio Serna-Garcés & Jorge Herrera & Asier Ibeas, 2017. "Maximum Power Point Tracking of Photovoltaic Panels by Using Improved Pattern Search Methods," Energies, MDPI, vol. 10(9), pages 1-15, September.
    13. Yi Jin & Wenhui Hou & Guiqiang Li & Xiao Chen, 2017. "A Glowworm Swarm Optimization-Based Maximum Power Point Tracking for Photovoltaic/Thermal Systems under Non-Uniform Solar Irradiation and Temperature Distribution," Energies, MDPI, vol. 10(4), pages 1-13, April.
    14. Jian Zhao & Xuesong Zhou & Youjie Ma & Yiqi Liu, 2017. "Analysis of Dynamic Characteristic for Solar Arrays in Series and Global Maximum Power Point Tracking Based on Optimal Initial Value Incremental Conductance Strategy under Partially Shaded Conditions," Energies, MDPI, vol. 10(1), pages 1-23, January.
    15. Nebiyu Kedir & Phuong H. D. Nguyen & Citlaly Pérez & Pedro Ponce & Aminah Robinson Fayek, 2023. "Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation," Energies, MDPI, vol. 16(9), pages 1-38, April.
    16. Tingting Pei & Xiaohong Hao & Qun Gu, 2018. "A Novel Global Maximum Power Point Tracking Strategy Based on Modified Flower Pollination Algorithm for Photovoltaic Systems under Non-Uniform Irradiation and Temperature Conditions," Energies, MDPI, vol. 11(10), pages 1-16, October.
    17. Lalili, D. & Mellit, A. & Lourci, N. & Medjahed, B. & Berkouk, E.M., 2011. "Input output feedback linearization control and variable step size MPPT algorithm of a grid-connected photovoltaic inverter," Renewable Energy, Elsevier, vol. 36(12), pages 3282-3291.
    18. 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.
    19. Jordehi, A. Rezaee, 2016. "Maximum power point tracking in photovoltaic (PV) systems: A review of different approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1127-1138.
    20. 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.

    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:8:y:2015:i:6:p:5338-5360:d:50686. 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.