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A Novel Hybrid MPPT Approach for Solar PV Systems Using Particle-Swarm-Optimization-Trained Machine Learning and Flying Squirrel Search Optimization

Author

Listed:
  • Dilip Kumar

    (Department of Electrical Engineering, Institute of Engineering and Technology, Dr. Rammanohar Lohia Avadh University, Ayodhya 224001, India)

  • Yogesh Kumar Chauhan

    (Department of Electrical Engineering, Kamla Nehru Institute of Engineering and Technology, Sultanpur 228118, India)

  • Ajay Shekhar Pandey

    (Department of Electrical Engineering, Kamla Nehru Institute of Engineering and Technology, Sultanpur 228118, India)

  • Ankit Kumar Srivastava

    (Department of Electrical Engineering, Institute of Engineering and Technology, Dr. Rammanohar Lohia Avadh University, Ayodhya 224001, India)

  • Varun Kumar

    (Department of Electrical Engineering, Kamla Nehru Institute of Engineering and Technology, Sultanpur 228118, India)

  • Faisal Alsaif

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

  • Rajvikram Madurai Elavarasan

    (Research & Development Division (Power & Energy), Nestlives Private Limited, Chennai 600091, India)

  • Md Rabiul Islam

    (School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia)

  • Raju Kannadasan

    (Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Sriperumbudur, Chennai 602117, India)

  • Mohammed H. Alsharif

    (Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Seoul 05006, Republic of Korea)

Abstract

In this paper, a novel hybrid Maximum Power Point Tracking (MPPT) algorithm using Particle-Swarm-Optimization-trained machine learning and Flying Squirrel Search Optimization (PSO_ML-FSSO) has been proposed to obtain the optimal efficiency for solar PV systems. The proposed algorithm was compared with other well-known methods viz. Perturb & Observer (P&O), Incremental Conductance (INC), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CSO), Flower Pollen Algorithm (FPA), Gray Wolf Optimization (GWO), Neural-Network-trained Machine Learning (NN_ML), Genetic Algorithm (GA), and PSO-trained Machine Learning. The proposed algorithm was modelled in the MATLAB/Simulink environment under different operating conditions, for example, with step changes in temperature, solar irradiance, and partial shading. The proposed algorithm improved the efficiency up to 0.72% and reduced the settling time up to 76.4%. The findings of the research highlight that PSO_ML-FSSO is a potential approach that outperforms all other well-known algorithms tested herein for solar PV systems.

Suggested Citation

  • Dilip Kumar & Yogesh Kumar Chauhan & Ajay Shekhar Pandey & Ankit Kumar Srivastava & Varun Kumar & Faisal Alsaif & Rajvikram Madurai Elavarasan & Md Rabiul Islam & Raju Kannadasan & Mohammed H. Alshari, 2023. "A Novel Hybrid MPPT Approach for Solar PV Systems Using Particle-Swarm-Optimization-Trained Machine Learning and Flying Squirrel Search Optimization," Sustainability, MDPI, vol. 15(6), pages 1-29, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5575-:d:1104117
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    References listed on IDEAS

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    1. Efrain Mendez & Alexandro Ortiz & Pedro Ponce & Israel Macias & David Balderas & Arturo Molina, 2020. "Improved MPPT Algorithm for Photovoltaic Systems Based on the Earthquake Optimization Algorithm," Energies, MDPI, vol. 13(12), pages 1-24, June.
    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. Ahmed, Jubaer & Salam, Zainal, 2014. "A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability," Applied Energy, Elsevier, vol. 119(C), pages 118-130.
    4. Ashwin Kumar Devarakonda & Natarajan Karuppiah & Tamilselvi Selvaraj & Praveen Kumar Balachandran & Ravivarman Shanmugasundaram & Tomonobu Senjyu, 2022. "A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems," Energies, MDPI, vol. 15(22), pages 1-30, November.
    5. Ehab Mohamed Ali & Ahmed K. Abdelsalam & Karim H. Youssef & Ahmed A. Hossam-Eldin, 2021. "An Enhanced Cuckoo Search Algorithm Fitting for Photovoltaic Systems’ Global Maximum Power Point Tracking under Partial Shading Conditions," Energies, MDPI, vol. 14(21), pages 1-21, November.
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