IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i9p3788-d1386774.html
   My bibliography  Save this article

An Optimization Algorithm for Embedded Raspberry Pi Pico Controllers for Solar Tree Systems

Author

Listed:
  • K. Punitha

    (Department of Electrical and Electronics Engineering, P. S. R. Engineering College, Sivakasi 626140, Tamil Nadu, India)

  • Akhlaqur Rahman

    (Department of Electrical Engineering and Industrial Automation, Engineering Institute of Technology, Melbourne Campus, Melbourne, VIC 3000, Australia)

  • A. S. Radhamani

    (Department of Electronics and Communication Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Nagercoil 629901, Tamil Nadu, India)

  • Ramakrishna S. S. Nuvvula

    (Department of Electrical and Electronics Engineering, NMAM Institute of Technology, NITTE (Deemed to be University), Karkala 574110, Karnataka, India)

  • Sk. A. Shezan

    (Department of Electrical Engineering and Industrial Automation, Engineering Institute of Technology, Melbourne Campus, Melbourne, VIC 3000, Australia)

  • Syed Riyaz Ahammed

    (Department of Electronics & Communication Engineering, NMAM Institute of Technology, NITTE (Deemed to be University), Karkala 574110, Karnataka, India)

  • Polamarasetty P. Kumar

    (Department of Electrical and Electronics Engineering, GMR Institute of Technology, Razam 532127, Andhra Pradesh, India)

  • Md Fatin Ishraque

    (Department of Electrical, Electronics and Communication Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh)

Abstract

Solar photovoltaic (PV) systems stand out as a promising solution for generating clean, carbon-free energy. However, traditional solar panel installations often require extensive land resources, which could become scarce as the population grows. To address this challenge, innovative approaches are needed to maximize solar power generation within limited spaces. One promising concept involves the development of biological tree-like structures housing solar panels. These “solar trees” mimic the arrangement of branches and leaves found in natural trees, following patterns akin to phyllotaxy, which correlates with the Fibonacci sequence and golden ratio. By adopting an alternative 1:3 phyllotaxy pattern, three solar panels can be efficiently arranged along the stem of the solar tree structure, each rotated at a 120-degree displacement. Optimizing the performance of solar trees requires effective maximum power point tracking (MPPT), a crucial process for extracting the maximum available power from solar panels to enhance the overall efficiency. In this study, a novel metaheuristic algorithm called horse herd optimization (HHO) is employed for MPPT in solar tree applications. Moreover, to efficiently manage the generated power, a cascaded buck–boost converter is utilized. This converter is capable of adjusting the DC voltage levels to match the system requirements within a single topology. The algorithm is implemented using MATLAB and embedded within a Raspberry Pi Pico controller, which facilitates the generation of pulse-width modulation (PWM) signals to control the cascaded buck–boost converter. Through extensive validation, this study confirms the effectiveness of the proposed HHO algorithm integrated into the Raspberry Pi Pico controller for optimizing solar trees under various shading conditions. In essence, this research highlights the potential of solar tree structures coupled with advanced MPPT algorithms and power management systems to maximize solar energy utilization, offering a sustainable solution for clean energy generation within limited land resources.

Suggested Citation

  • K. Punitha & Akhlaqur Rahman & A. S. Radhamani & Ramakrishna S. S. Nuvvula & Sk. A. Shezan & Syed Riyaz Ahammed & Polamarasetty P. Kumar & Md Fatin Ishraque, 2024. "An Optimization Algorithm for Embedded Raspberry Pi Pico Controllers for Solar Tree Systems," Sustainability, MDPI, vol. 16(9), pages 1-26, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3788-:d:1386774
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/9/3788/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/9/3788/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rizzo, Santi Agatino & Scelba, Giacomo, 2015. "ANN based MPPT method for rapidly variable shading conditions," Applied Energy, Elsevier, vol. 145(C), pages 124-132.
    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. Obeidi, Nabil & Kermadi, Mostefa & Belmadani, Bachir & Allag, Abdelkrim & Achour, Lazhar & Mesbahi, Nadhir & Mekhilef, Saad, 2023. "A modified current sensorless approach for maximum power point tracking of partially shaded photovoltaic systems," Energy, Elsevier, vol. 263(PA).
    2. Ma, Jun & Cheng, Jack C.P., 2016. "Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests," Applied Energy, Elsevier, vol. 183(C), pages 193-201.
    3. Waleed Al Abri & Rashid Al Abri & Hassan Yousef & Amer Al-Hinai, 2021. "A Simple Method for Detecting Partial Shading in PV Systems," Energies, MDPI, vol. 14(16), pages 1-12, August.
    4. Abderrazek Saoudi & Saber Krim & Mohamed Faouzi Mimouni, 2021. "Enhanced Intelligent Closed Loop Direct Torque and Flux Control of Induction Motor for Standalone Photovoltaic Water Pumping System," Energies, MDPI, vol. 14(24), pages 1-21, December.
    5. Celikel, Resat & Yilmaz, Musa & Gundogdu, Ahmet, 2022. "A voltage scanning-based MPPT method for PV power systems under complex partial shading conditions," Renewable Energy, Elsevier, vol. 184(C), pages 361-373.
    6. Guo, Lei & Meng, Zhuo & Sun, Yize & Wang, Libiao, 2018. "A modified cat swarm optimization based maximum power point tracking method for photovoltaic system under partially shaded condition," Energy, Elsevier, vol. 144(C), pages 501-514.
    7. Başoğlu, Mustafa Engin & Çakır, Bekir, 2016. "Comparisons of MPPT performances of isolated and non-isolated DC–DC converters by using a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1100-1113.
    8. Anjan Debnath & Temitayo O. Olowu & Imtiaz Parvez & Md Golam Dastgir & Arif Sarwat, 2020. "A Novel Module Independent Straight Line-Based Fast Maximum Power Point Tracking Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 13(12), pages 1-15, June.
    9. Wang, Qin & Yao, Wei & Fang, Jiakun & Ai, Xiaomeng & Wen, Jinyu & Yang, Xiaobo & Xie, Hailian & Huang, Xing, 2020. "Dynamic modeling and small signal stability analysis of distributed photovoltaic grid-connected system with large scale of panel level DC optimizers," Applied Energy, Elsevier, vol. 259(C).
    10. Fathabadi, Hassan, 2016. "Novel fast dynamic MPPT (maximum power point tracking) technique with the capability of very high accurate power tracking," Energy, Elsevier, vol. 94(C), pages 466-475.
    11. 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.
    12. Fathabadi, Hassan, 2016. "Novel highly accurate universal maximum power point tracker for maximum power extraction from hybrid fuel cell/photovoltaic/wind power generation systems," Energy, Elsevier, vol. 116(P1), pages 402-416.
    13. Belkaid, A. & Colak, I. & Isik, O., 2016. "Photovoltaic maximum power point tracking under fast varying of solar radiation," Applied Energy, Elsevier, vol. 179(C), pages 523-530.
    14. Belhachat, Faiza & Larbes, Cherif, 2017. "Global maximum power point tracking based on ANFIS approach for PV array configurations under partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 875-889.
    15. Abu Eldahab, Yasser E. & Saad, Naggar H. & Zekry, Abdalhalim, 2017. "Enhancing the tracking techniques for the global maximum power point under partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1173-1183.
    16. Fathi Troudi & Houda Jouini & Abdelkader Mami & Nidhal Ben Khedher & Walid Aich & Attia Boudjemline & Mohamed Boujelbene, 2022. "Comparative Assessment between Five Control Techniques to Optimize the Maximum Power Point Tracking Procedure for PV Systems," Mathematics, MDPI, vol. 10(7), pages 1-15, March.
    17. Qiu, Changyu & Yi, Yun Kyu & Wang, Meng & Yang, Hongxing, 2020. "Coupling an artificial neuron network daylighting model and building energy simulation for vacuum photovoltaic glazing," Applied Energy, Elsevier, vol. 263(C).
    18. Ahmad M. A. Malkawi & Abdallah Odat & Ahmad Bashaireh, 2022. "A Novel PV Maximum Power Point Tracking Based on Solar Irradiance and Circuit Parameters Estimation," Sustainability, MDPI, vol. 14(13), pages 1-14, June.
    19. Strušnik, Dušan & Marčič, Milan & Golob, Marjan & Hribernik, Aleš & Živić, Marija & Avsec, Jurij, 2016. "Energy efficiency analysis of steam ejector and electric vacuum pump for a turbine condenser air extraction system based on supervised machine learning modelling," Applied Energy, Elsevier, vol. 173(C), pages 386-405.
    20. Hong, Ying-Yi & Beltran, Angelo A. & Paglinawan, Arnold C., 2018. "A robust design of maximum power point tracking using Taguchi method for stand-alone PV system," Applied Energy, Elsevier, vol. 211(C), pages 50-63.

    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:jsusta:v:16:y:2024:i:9:p:3788-:d:1386774. 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.