Learning of Maximum Power Point Tracking Architecture with Various Algorithms for Photovoltaic Systems: A Review

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Year : 2026 | Volume : 16 | 02 | Page :
    By

    Goraksha B Polade,

  • Umesh Kute,

  1. PhD Scholar, Department of Electrical engineering, Pimpri Chinchwad University, Pune, Maharashtra, India
  2. Assistant Professor, Department of Electrical engineering, Pimpri Chinchwad University, Pune, Maharashtra, India

Abstract

Currently, as the requirement on the Earth for ever more electricity grows, so too accordingly must demands upon renewable energy. These days, with the growth of renewable energy on all fronts, countries everywhere watch its development. Since demand for power generation goes up again, fossil fuels become less and less available, and expense is not coming down. When there is a rapidly changing irradiance, temperature, or partial shading, the output power from PV systems varies dramatically. In contrast, conventional MPPT techniques like P & O and INC have slow convergence rates. They are prone to steady-state oscillations, adapting poorly as the environment changes rapidly. For this reason, advanced AI-based MPPT approaches bring an improved level of tracking, most are based on a static neural network structure. Under complicated operating situations, these techniques provide improved capacity to determine the maximum global power point. When handling extremely dynamic environmental variables, their performance could still be constrained. As a result, intelligent and adaptable MPPT frameworks that can make decisions in real time and learn continuously are crucial. These methods may greatly enhance tracking accuracy, lower power losses, boost energy conversion efficiency, and guarantee steady PV system operation, all of which help meet the world’s expanding need for dependable and sustainable renewable energy production. Under complicated operating situations, these techniques provide improved capacity to determine the maximum global power point. When handling extremely dynamic environmental variables, their performance could still be constrained. As a result, intelligent and adaptable MPPT frameworks that can make decisions in real time and learn continuously are crucial. These methods may greatly enhance tracking accuracy, lower power losses, boost energy conversion efficiency, and guarantee steady PV system operation, all of which help meet the world’s expanding need for dependable and sustainable renewable energy production.

Keywords: Efficiency, Maximum-power point tracking, Perturb and Observe, Partial Shading Conditions, Photovoltaic Systems,

How to cite this article:
Goraksha B Polade, Umesh Kute. Learning of Maximum Power Point Tracking Architecture with Various Algorithms for Photovoltaic Systems: A Review. Journal of Power Electronics and Power Systems. 2026; 16(02):-.
How to cite this URL:
Goraksha B Polade, Umesh Kute. Learning of Maximum Power Point Tracking Architecture with Various Algorithms for Photovoltaic Systems: A Review. Journal of Power Electronics and Power Systems. 2026; 16(02):-. Available from: https://journals.stmjournals.com/jopeps/article=2026/view=247590


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Ahead of Print Subscription Review Article
Volume 16
02
Received 29/05/2026
Accepted 24/06/2026
Published 25/06/2026
Publication Time 27 Days
100

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