IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v124y2020ics1364032120300174.html
   My bibliography  Save this article

Simulation and experimental validation of fast adaptive particle swarm optimization strategy for photovoltaic global peak tracker under dynamic partial shading

Author

Listed:
  • Eltamaly, Ali M.
  • Al-Saud, M.S.
  • Abokhalil, Ahmed G.
  • Farh, Hassan M.H.

Abstract

The P–V characteristics of PV array has one peak under uniformly distributed irradiances. Whereas, there are many peaks in the P–V curve when the irradiance is not uniformly distributed over the PV array which is called “partial shading conditions (PSCs)”. Due to its robustness in tracking the global peak (GP) of many applications, metaheuristic techniques are used as maximum power point tracker (MPPT) for the PV system under PSCs. Particle swarm optimization (PSO) has been used in this paper for this purpose. Three problems associated with the PSO have been solved in this paper using a novel fast adaptive PSO (APSO) strategy. The problem of long convergence time has been solved by updating starting values of the duty ratio of the DC-DC boost converter to be at the anticipated places of peaks. This modification reduces the convergence time and avoids the premature convergence. The problem of stored GP in the memory will prevent the PSO from capturing the current GP in case of it is lower than the stored one. This problem is solved in this paper by updating the memorized GP with the current maximum power when it is not changed for two successive iterations. The third problem of sudden change in PSCs is solved by using the updated values of duty ratio at anticipated peaks as initial values for particles. To the best of the authors’ knowledge, these problems have not been discussed or solved before in the literature. A comparison to the state-of-the-art random initialization PSO strategy shows the superiority of the proposed APSO technique in terms of tracking speed and dynamic GP tracking. The results obtained from the simulation of this strategy proved its superiority in always tracking the GP under dynamic PSCs change.

Suggested Citation

  • Eltamaly, Ali M. & Al-Saud, M.S. & Abokhalil, Ahmed G. & Farh, Hassan M.H., 2020. "Simulation and experimental validation of fast adaptive particle swarm optimization strategy for photovoltaic global peak tracker under dynamic partial shading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    • Handle: RePEc:eee:rensus:v:124:y:2020:i:c:s1364032120300174
    DOI: 10.1016/j.rser.2020.109719
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032120300174
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2020.109719?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ali M. Eltamaly & Hassan M. H. Farh & Mamdooh S. Al Saud, 2019. "Impact of PSO Reinitialization on the Accuracy of Dynamic Global Maximum Power Detection of Variant Partially Shaded PV Systems," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
    2. 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.
    3. Hassan M. H. Farh & Mohd F. Othman & Ali M. Eltamaly & M. S. Al-Saud, 2018. "Maximum Power Extraction from a Partially Shaded PV System Using an Interleaved Boost Converter," Energies, MDPI, vol. 11(10), pages 1-18, September.
    4. 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.
    5. Ali M. Eltamaly & M. S. Al-Saud & A. G. Abo-Khalil, 2020. "Performance Improvement of PV Systems’ Maximum Power Point Tracker Based on a Scanning PSO Particle Strategy," Sustainability, MDPI, vol. 12(3), pages 1-20, February.
    6. Prasanth Ram, J. & Rajasekar, N., 2017. "A new robust, mutated and fast tracking LPSO method for solar PV maximum power point tracking under partial shaded conditions," Applied Energy, Elsevier, vol. 201(C), pages 45-59.
    7. Ahmed, Jubaer & Salam, Zainal, 2015. "A critical evaluation on maximum power point tracking methods for partial shading in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 933-953.
    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. Majed A. Alotaibi & Ali M. Eltamaly, 2021. "A Smart Strategy for Sizing of Hybrid Renewable Energy System to Supply Remote Loads in Saudi Arabia," Energies, MDPI, vol. 14(21), pages 1-24, October.
    2. Novie Ayub Windarko & Muhammad Nizar Habibi & Bambang Sumantri & Eka Prasetyono & Moh. Zaenal Efendi & Taufik, 2021. "A New MPPT Algorithm for Photovoltaic Power Generation under Uniform and Partial Shading Conditions," Energies, MDPI, vol. 14(2), pages 1-22, January.
    3. Tabassum Kanwal & Saif Ur Rehman & Tariq Ali & Khalid Mahmood & Santos Gracia Villar & Luis Alonso Dzul Lopez & Imran Ashraf, 2023. "An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field," Agriculture, MDPI, vol. 13(8), pages 1-19, August.
    4. Abdulaziz Almutairi & Ahmed G. Abo-Khalil & Khairy Sayed & Naif Albagami, 2020. "MPPT for a PV Grid-Connected System to Improve Efficiency under Partial Shading Conditions," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    5. Ghazi A. Ghazi & Hany M. Hasanien & Essam A. Al-Ammar & Rania A. Turky & Wonsuk Ko & Sisam Park & Hyeong-Jin Choi, 2022. "African Vulture Optimization Algorithm-Based PI Controllers for Performance Enhancement of Hybrid Renewable-Energy Systems," Sustainability, MDPI, vol. 14(13), pages 1-26, July.
    6. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    7. Tamir Shaqarin, 2023. "Particle Swarm Optimization with Targeted Position-Mutated Elitism (PSO-TPME) for Partially Shaded PV Systems," Sustainability, MDPI, vol. 15(5), pages 1-23, February.
    8. Gao, Fang & Hu, Rongzhao & Yin, Linfei, 2023. "Variable boundary reinforcement learning for maximum power point tracking of photovoltaic grid-connected systems," Energy, Elsevier, vol. 264(C).
    9. Ahmed G. Abo-Khalil & Walied Alharbi & Abdel-Rahman Al-Qawasmi & Mohammad Alobaid & Ibrahim M. Alarifi, 2021. "Maximum Power Point Tracking of PV Systems under Partial Shading Conditions Based on Opposition-Based Learning Firefly Algorithm," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
    10. Ali M. Eltamaly, 2021. "A Novel Strategy for Optimal PSO Control Parameters Determination for PV Energy Systems," Sustainability, MDPI, vol. 13(2), pages 1-28, January.
    11. Sajid, Injila & Sarwar, Adil & Tariq, Mohd & Bakhsh, Farhad Ilahi & Ahmad, Shafiq & Shah Noor Mohamed, Adamali, 2023. "Archimedes optimization algorithm (AOA)-Based global maximum power point tracking for a photovoltaic system under partial and complex shading conditions," Energy, Elsevier, vol. 283(C).
    12. Chanuri Charin & Dahaman Ishak & Muhammad Ammirrul Atiqi Mohd Zainuri & Baharuddin Ismail & Turki Alsuwian & Adam R. H. Alhawari, 2022. "Modified Levy-based Particle Swarm Optimization (MLPSO) with Boost Converter for Local and Global Point Tracking," Energies, MDPI, vol. 15(19), pages 1-30, October.
    13. Alfredo Gil-Velasco & Carlos Aguilar-Castillo, 2021. "A Modification of the Perturb and Observe Method to Improve the Energy Harvesting of PV Systems under Partial Shading Conditions," Energies, MDPI, vol. 14(9), pages 1-12, April.
    14. Imran Pervez & Charalampos Antoniadis & Yehia Massoud, 2022. "Advanced Limited Search Strategy for Enhancing the Performance of MPPT Algorithms," Energies, MDPI, vol. 15(15), pages 1-19, August.
    15. Eltamaly, Ali M., 2021. "A novel musical chairs algorithm applied for MPPT of PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    16. Mohamed Zaghloul-El Masry & Abdallah Mohammed & Fathy Amer & Roaa Mubarak, 2023. "New Hybrid MPPT Technique Including Artificial Intelligence and Traditional Techniques for Extracting the Global Maximum Power from Partially Shaded PV Systems," Sustainability, MDPI, vol. 15(14), pages 1-30, July.
    17. Ali M. Eltamaly, 2021. "An Improved Cuckoo Search Algorithm for Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Conditions," Energies, MDPI, vol. 14(4), pages 1-26, February.

    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. Adel O. Baatiah & Ali M. Eltamaly & Majed A. Alotaibi, 2023. "Improving Photovoltaic MPPT Performance through PSO Dynamic Swarm Size Reduction," Energies, MDPI, vol. 16(18), pages 1-15, September.
    2. Ahmed G. Abo-Khalil & Walied Alharbi & Abdel-Rahman Al-Qawasmi & Mohammad Alobaid & Ibrahim M. Alarifi, 2021. "Maximum Power Point Tracking of PV Systems under Partial Shading Conditions Based on Opposition-Based Learning Firefly Algorithm," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
    3. Ali M. Eltamaly, 2021. "A Novel Strategy for Optimal PSO Control Parameters Determination for PV Energy Systems," Sustainability, MDPI, vol. 13(2), pages 1-28, January.
    4. Fahd A. Alturki & Abdullrahman A. Al-Shamma’a & Hassan M. H. Farh, 2020. "Simulations and dSPACE Real-Time Implementation of Photovoltaic Global Maximum Power Extraction under Partial Shading," Sustainability, MDPI, vol. 12(9), pages 1-16, May.
    5. Dalia Yousri & Thanikanti Sudhakar Babu & Dalia Allam & Vigna. K. Ramachandaramurthy & Eman Beshr & Magdy. B. Eteiba, 2019. "Fractional Chaos Maps with Flower Pollination Algorithm for Partial Shading Mitigation of Photovoltaic Systems," Energies, MDPI, vol. 12(18), pages 1-27, September.
    6. Mohamed Zaghloul-El Masry & Abdallah Mohammed & Fathy Amer & Roaa Mubarak, 2023. "New Hybrid MPPT Technique Including Artificial Intelligence and Traditional Techniques for Extracting the Global Maximum Power from Partially Shaded PV Systems," Sustainability, MDPI, vol. 15(14), pages 1-30, July.
    7. Rezk, Hegazy & AL-Oran, Mazen & Gomaa, Mohamed R. & Tolba, Mohamed A. & Fathy, Ahmed & Abdelkareem, Mohammad Ali & Olabi, A.G. & El-Sayed, Abou Hashema M., 2019. "A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    8. Novie Ayub Windarko & Muhammad Nizar Habibi & Bambang Sumantri & Eka Prasetyono & Moh. Zaenal Efendi & Taufik, 2021. "A New MPPT Algorithm for Photovoltaic Power Generation under Uniform and Partial Shading Conditions," Energies, MDPI, vol. 14(2), pages 1-22, January.
    9. Ali M. Eltamaly & M. S. Al-Saud & A. G. Abo-Khalil, 2020. "Performance Improvement of PV Systems’ Maximum Power Point Tracker Based on a Scanning PSO Particle Strategy," Sustainability, MDPI, vol. 12(3), pages 1-20, February.
    10. Zhi-Kai Fan & Kuo-Lung Lian & Jia-Fu Lin, 2023. "A New Golden Eagle Optimization with Stooping Behaviour for Photovoltaic Maximum Power Tracking under Partial Shading," Energies, MDPI, vol. 16(15), pages 1-19, July.
    11. Kuei-Hsiang Chao & Muhammad Nursyam Rizal, 2021. "A Hybrid MPPT Controller Based on the Genetic Algorithm and Ant Colony Optimization for Photovoltaic Systems under Partially Shaded Conditions," Energies, MDPI, vol. 14(10), pages 1-17, May.
    12. Ali M. Eltamaly & Hassan M. H. Farh & Mamdooh S. Al Saud, 2019. "Impact of PSO Reinitialization on the Accuracy of Dynamic Global Maximum Power Detection of Variant Partially Shaded PV Systems," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
    13. Ali M. Eltamaly & Majed A. Alotaibi, 2024. "Novel Hybrid Mexican Axolotl Optimization with Fuzzy Logic for Maximum Power Point Tracker of Partially Shaded Photovoltaic Systems," Energies, MDPI, vol. 17(11), pages 1-25, May.
    14. Ali M. Eltamaly, 2021. "An Improved Cuckoo Search Algorithm for Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Conditions," Energies, MDPI, vol. 14(4), pages 1-26, February.
    15. Ali M. Eltamaly & Zeyad A. Almutairi & Mohamed A. Abdelhamid, 2023. "Modern Optimization Algorithm for Improved Performance of Maximum Power Point Tracker of Partially Shaded PV Systems," Energies, MDPI, vol. 16(13), pages 1-22, July.
    16. Astitva Kumar & Mohammad Rizwan & Uma Nangia & Muhannad Alaraj, 2021. "Grey Wolf Optimizer-Based Array Reconfiguration to Enhance Power Production from Solar Photovoltaic Plants under Different Scenarios," Sustainability, MDPI, vol. 13(24), pages 1-18, December.
    17. Changsheng Yuan & Xuesong Zhou & Youjie Ma & Zhiqiang Gao & Yongliang Zhou & Chenglong Wang, 2020. "Improved Application of Third-Order LADRC in Wind Power Inverter," Energies, MDPI, vol. 13(17), pages 1-22, August.
    18. 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.
    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. Zhicheng Lin & Song Zheng & Zhicheng Chen & Rong Zheng & Wang Zhang, 2019. "Application Research of the Parallel System Theory and the Data Engine Approach in Wind Energy Conversion System," Energies, MDPI, vol. 12(5), pages 1-20, March.

    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:eee:rensus:v:124:y:2020:i:c:s1364032120300174. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.