Performance Analysis of an AIML-Driven MPPT Algorithm Using Metaheuristic Optimization Techniques for Improved Solar PV System Efficiency Under Varied Operating Conditions

  • Unique Paper ID: 171427
  • PageNo: 3010-3018
  • Abstract:
  • Metaheuristic algorithms have emerged as powerful tools for solving complex optimization problems across diverse domains. This paper presents a comprehensive literature review of recent advancements in metaheuristic optimization techniques, with a particular focus on their application in solar photovoltaic (PV) systems. The study explores a variety of algorithms, including the latest innovations such as the Walrus Optimization Algorithm, Sewing Training-Based Optimization, Osprey Optimization Algorithm, and Secretary Bird Optimization Algorithm, highlighting their underlying principles, unique features, and performance metrics. The study examines the growing trend of integrating artificial intelligence (AI) and machine learning (ML) with metaheuristic techniques to enhance Maximum Power Point Tracking (MPPT) strategies for solar PV systems. Additionally, it identifies gaps in the current body of knowledge and discusses challenges such as computational complexity, convergence issues, and scalability.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{171427,
        author = {Mr. Vaibhav Sharma and Dr. Ankur Kumar Gupta and Mr. Akshay Raj},
        title = {Performance Analysis of an AIML-Driven MPPT Algorithm Using Metaheuristic Optimization Techniques for Improved Solar PV System Efficiency Under Varied Operating Conditions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {3010-3018},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171427},
        abstract = {Metaheuristic algorithms have emerged as powerful tools for solving complex optimization problems across diverse domains. This paper presents a comprehensive literature review of recent advancements in metaheuristic optimization techniques, with a particular focus on their application in solar photovoltaic (PV) systems. The study explores a variety of algorithms, including the latest innovations such as the Walrus Optimization Algorithm, Sewing Training-Based Optimization, Osprey Optimization Algorithm, and Secretary Bird Optimization Algorithm, highlighting their underlying principles, unique features, and performance metrics. The study examines the growing trend of integrating artificial intelligence (AI) and machine learning (ML) with metaheuristic techniques to enhance Maximum Power Point Tracking (MPPT) strategies for solar PV systems. Additionally, it identifies gaps in the current body of knowledge and discusses challenges such as computational complexity, convergence issues, and scalability.},
        keywords = {Metaheuristic Algorithms, Literature Review, Optimization Techniques, Solar Photovoltaic Systems, Maximum Power Point Tracking (MPPT), Artificial Intelligence, Machine Learning, Walrus Optimization Algorithm, Sewing Training-Based Optimization, Osprey Optimization Algorithm, Secretary Bird Optimization Algorithm, Renewable Energy, Hybrid Algorithms, Computational Complexity, Sustainable Energy Solutions.},
        month = {December},
        }

Cite This Article

Sharma, M. V., & Gupta, D. A. K., & Raj, M. A. (2024). Performance Analysis of an AIML-Driven MPPT Algorithm Using Metaheuristic Optimization Techniques for Improved Solar PV System Efficiency Under Varied Operating Conditions. International Journal of Innovative Research in Technology (IJIRT), 11(7), 3010–3018.

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