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@article{187040,
        author = {Panmale Monika Shrihari and Kolhe Pooja Tanaji and Pawar Shriram Vitthal and More Priyanka Ambadas and Gosavi Pooja Sunil},
        title = {Smart solar PV System with concentrated tracking and predictive MPPT Optimization},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {3843-3849},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187040},
        abstract = {Photovoltaic (PV) systems play a vital role in converting solar energy into electricity but often experience performance decline due to changing environmental conditions. To counter this, Maximum Power Point Tracking (MPPT) algorithms are employed to optimize PV system performance and ensure maximum power extraction under varying conditions. This review provides an extensive classification and analysis of MPPT strategies, grouping them into three main categories: classical, adaptive, and hybrid techniques. Classical methods such as Perturb and Observe (P&O) and Incremental Conductance (Inc Cond) are commonly used because of their simplicity and cost-effectiveness, though they struggle to maintain accuracy during rapid environmental fluctuations. Adaptive approaches, including Fuzzy Logic Controllers and Artificial Neural Networks, improve precision and adaptability but demand higher computational power. Hybrid techniques combine the benefits of both classical and adaptive methods to achieve a balance between stability and dynamic response. Additionally, the paper discusses the influence of factors such as temperature variations, changes in irradiance, and partial shading on PV system efficiency and MPPT performance. The critical assessment highlights the advantages and drawbacks of existing algorithms while identifying future opportunities for enhancing reliability and energy conversion efficiency.},
        keywords = {Solar Panel, Maximum Power Point Tracking (MPPT), Photovoltaic (PV) system, Renewable Energy, Artificial Neural Network, Fuzzy Logic Controllers.},
        month = {November},
        }