IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i4p979-d1068226.html
   My bibliography  Save this article

Modified Artificial Hummingbird Algorithm-Based Single-Sensor Global MPPT for Photovoltaic Systems

Author

Listed:
  • Hesham Alhumade

    (Chemical and Materials Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Center of Excellence in Desalination Technology, King Abdulaziz University, P.O. Box 80200, Jeddah 21589, Saudi Arabia
    K. A. CARE Energy Research and Innovation Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Essam H. Houssein

    (Faculty of Computers and Information, Minia University, Minia 61519, Egypt)

  • Hegazy Rezk

    (Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Wadi Alddawasir 11991, Saudi Arabia)

  • Iqbal Ahmed Moujdin

    (Center of Excellence in Desalination Technology, King Abdulaziz University, P.O. Box 80200, Jeddah 21589, Saudi Arabia
    Department of Mechanical Engineering, King Abdulaziz University, P.O. Box 80200, Jeddah 21589, Saudi Arabia)

  • Saad Al-Shahrani

    (Chemical and Materials Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

Recently, a swarm-based method called Artificial Hummingbird Algorithm (AHA) has been proposed for solving optimization problems. The AHA algorithm mimics the unique flight capabilities and intelligent foraging techniques of hummingbirds in their environment. In this paper, we propose a modified version of the AHA combined with genetic operators called mAHA. The experimental results show that the proposed mAHA improved the convergence speed and achieved better effective search results. Consequently, the proposed mAHA was used for the first time to find the global maximum power point (MPP). Low efficiency is a drawback of photovoltaic (PV) systems that explicitly use shading. Normally, the PV characteristic curve has an MPP when irradiance is uniform. Therefore, this MPP can be easily achieved with conventional tracking systems. With shadows, however, the conditions are completely different, and the PV characteristic has multiple MPPs (i.e., some local MPPs and a single global MPP). Traditional MPP tracking approaches cannot distinguish between local MPPs and global MPPs, and thus simply get stuck at the local MPP. Consequently, an optimized MPPT with a metaheuristic algorithm is required to determine the global MPP. Most MPPT techniques require more than one sensor, e.g., voltage, current, irradiance, and temperature sensors. This increases the cost of the control system. In the current research, a simple global MPPT method with only one sensor is proposed for PV systems considering the shadow conditions. Two shadow scenarios are considered to evaluate the superiority of the proposed mAHA. The obtained results show the superiority of the proposed single sensor based MPPT method for PV systems.

Suggested Citation

  • Hesham Alhumade & Essam H. Houssein & Hegazy Rezk & Iqbal Ahmed Moujdin & Saad Al-Shahrani, 2023. "Modified Artificial Hummingbird Algorithm-Based Single-Sensor Global MPPT for Photovoltaic Systems," Mathematics, MDPI, vol. 11(4), pages 1-25, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:979-:d:1068226
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/4/979/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/4/979/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu-Pei Huang & Cheng-En Ye & Xiang Chen, 2018. "A Modified Firefly Algorithm with Rapid Response Maximum Power Point Tracking for Photovoltaic Systems under Partial Shading Conditions," Energies, MDPI, vol. 11(9), pages 1-33, August.
    2. Tarek A. Boghdady & Yasmin E. Kotb & Abdullah Aljumah & Mahmoud M. Sayed, 2022. "Comparative Study of Optimal PV Array Configurations and MPPT under Partial Shading with Fast Dynamical Change of Hybrid Load," Sustainability, MDPI, vol. 14(5), pages 1-17, March.
    3. 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.
    4. Zhou, Yanting & Wang, Yanan & Wang, Kai & Kang, Le & Peng, Fei & Wang, Licheng & Pang, Jinbo, 2020. "Hybrid genetic algorithm method for efficient and robust evaluation of remaining useful life of supercapacitors," Applied Energy, Elsevier, vol. 260(C).
    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. 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).
    2. 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.
    3. 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.
    4. Haris, Muhammad & Hasan, Muhammad Noman & Qin, Shiyin, 2021. "Early and robust remaining useful life prediction of supercapacitors using BOHB optimized Deep Belief Network," Applied Energy, Elsevier, vol. 286(C).
    5. Ehtisham Lodhi & Fei-Yue Wang & Gang Xiong & Ghulam Ali Mallah & Muhammad Yaqoob Javed & Tariku Sinshaw Tamir & David Wenzhong Gao, 2021. "A Dragonfly Optimization Algorithm for Extracting Maximum Power of Grid-Interfaced PV Systems," Sustainability, MDPI, vol. 13(19), pages 1-27, September.
    6. Mariam A. Sameh & Mostafa I. Marei & M. A. Badr & Mahmoud A. Attia, 2021. "An Optimized PV Control System Based on the Emperor Penguin Optimizer," Energies, MDPI, vol. 14(3), pages 1-16, February.
    7. Kai Wang & Wanli Wang & Licheng Wang & Liwei Li, 2020. "An Improved SOC Control Strategy for Electric Vehicle Hybrid Energy Storage Systems," Energies, MDPI, vol. 13(20), pages 1-13, October.
    8. Sajid Sarwar & Muhammad Yaqoob Javed & Mujtaba Hussain Jaffery & Muhammad Saqib Ashraf & Muhammad Talha Naveed & Muhammad Annas Hafeez, 2022. "Modular Level Power Electronics (MLPE) Based Distributed PV System for Partial Shaded Conditions," Energies, MDPI, vol. 15(13), pages 1-39, June.
    9. Chen, Xiyu & Li, Qingbo & Shao, Bohan & Dou, Weilin & Lai, Chunyan & Lu, Taolin & Xie, Jingying, 2025. "Accurately estimating internal temperature of lithium-ion batteries based on the distribution of relaxation time and data-driven," Energy, Elsevier, vol. 320(C).
    10. Liu, Chunli & Li, Qiang & Wang, Kai, 2021. "State-of-charge estimation and remaining useful life prediction of supercapacitors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    11. Osmani, Khaled & Haddad, Ahmad & Lemenand, Thierry & Castanier, Bruno & Ramadan, Mohamad, 2021. "An investigation on maximum power extraction algorithms from PV systems with corresponding DC-DC converters," Energy, Elsevier, vol. 224(C).
    12. Ibrahim Al-Wesabi & Zhijian Fang & Hassan M. Hussein Farh & Abdullrahman A. Al-Shamma’a & Abdullah M. Al-Shaalan & Tarek Kandil & Min Ding, 2022. "Cuckoo Search Combined with PID Controller for Maximum Power Extraction of Partially Shaded Photovoltaic System," Energies, MDPI, vol. 15(7), pages 1-26, March.
    13. Shaowu Li & Kunyi Chen & Qin Li & Qing Ai, 2022. "A Variable-Weather-Parameter MPPT Method Based on Equation Solution for Photovoltaic System with DC Bus," Energies, MDPI, vol. 15(18), pages 1-25, September.
    14. Yang Meng & Zunliang Chen & Hui Cheng & Enpu Wang & Baohua Tan, 2023. "An Efficient Variable Step Solar Maximum Power Point Tracking Algorithm," Energies, MDPI, vol. 16(3), pages 1-20, January.
    15. 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.
    16. Soualhi, Moncef & El Koujok, Mohamed & Nguyen, Khanh T.P. & Medjaher, Kamal & Ragab, Ahmed & Ghezzaz, Hakim & Amazouz, Mouloud & Ouali, Mohamed-Salah, 2021. "Adaptive prognostics in a controlled energy conversion process based on long- and short-term predictors," Applied Energy, Elsevier, vol. 283(C).
    17. You-Da Jhong & Chang-Shian Chen & Bing-Chen Jhong & Cheng-Han Tsai & Song-Yue Yang, 2024. "Optimization of LSTM Parameters for Flash Flood Forecasting Using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(3), pages 1141-1164, February.
    18. Wael Korani & Malek Mouhoub, 2021. "Review on Nature-Inspired Algorithms," SN Operations Research Forum, Springer, vol. 2(3), pages 1-26, September.
    19. Waqar Akram, M. & Li, Guiqiang & Jin, Yi & Chen, Xiao, 2022. "Failures of Photovoltaic modules and their Detection: A Review," Applied Energy, Elsevier, vol. 313(C).
    20. Wang, Chenxu & Xiong, Rui & Tian, Jinpeng & Lu, Jiahuan & Zhang, Chengming, 2022. "Rapid ultracapacitor life prediction with a convolutional neural network," Applied Energy, Elsevier, vol. 305(C).

    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:jmathe:v:11:y:2023:i:4:p:979-:d:1068226. 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.