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Impact of PSO Reinitialization on the Accuracy of Dynamic Global Maximum Power Detection of Variant Partially Shaded PV Systems

Author

Listed:
  • Ali M. Eltamaly

    (Sustainable Energy Technologies Center, King Saud University, Riyadh 11421, Saudi Arabia
    Electrical Engineering Department, Mansoura University, Mansoura 35516, Egypt)

  • Hassan M. H. Farh

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Mamdooh S. Al Saud

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
    Saudi Electricity Company Chair in Power System Reliability and Security, King Saud University, Riyadh 11421, Saudi Arabia)

Abstract

The Global Maximum Power (GMP) of photovoltaic (PV) systems changes its location on the power–voltage (P–V) curve as the shading pattern (SP) changes over time. Although the original Particle Swarm Optimization (PSO) technique can catch the GMP easily under the same SP, once it changes its location, it cannot catch the new GMP because the particles search around the first GMP caught. Therefore, conventional PSO is a time-invariant GMP tracker that cannot follow the dynamic GMP under variant SP. The novelty in this study is the modification of the conventional PSO technique to become a time-variant GMP technique. This has been achieved through dispersing the particles based on two new reinitialization methodologies for searching for the variant GMP. The first methodology depends on dispersing the PSO particles at a certain predefined time (PDT) in order to look for the new GMP of the new SP. The latter depends on continually monitoring any changes in the SP to disperse the particles to follow the new GMP. A detailed comparison between the improved PSO with two new reinitialization methodologies and the conventional PSO is introduced. The improved PSO with SP change reinitialization methodology tracked the dynamic GMP efficiently and accurately compared the conventional PSO and the improved PSO with PDT reinitialization. Also, no hardware modification in the existing PV system is required, which makes it an excellent option to improve the performance of new and existing PV systems.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:7:p:2091-:d:220943
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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).
    3. Hegazy Rezk & Ahmed Fathy, 2020. "Stochastic Fractal Search Optimization Algorithm Based Global MPPT for Triple-Junction Photovoltaic Solar System," Energies, MDPI, vol. 13(18), pages 1-28, September.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Yuanzhuo Du & Kun Zhang & Qianzhi Shao & Zhe Chen, 2023. "A Short-Term Prediction Model of Wind Power with Outliers: An Integration of Long Short-Term Memory, Ensemble Empirical Mode Decomposition, and Sample Entropy," Sustainability, MDPI, vol. 15(7), pages 1-15, April.
    9. Enci Liu & Jie Li & Anni Zheng & Haoran Liu & Tao Jiang, 2022. "Research on the Prediction Model of the Used Car Price in View of the PSO-GRA-BP Neural Network," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    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. 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.
    12. 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.
    13. 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).
    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.

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