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@article{202112,
        author = {TOFIK LATIF MULANI and NAJIYA ISSAK SHAIKH and Prof.Suraj S.Shinde},
        title = {Soft Computing Based Hybrid MPPT Controller using ANN and INC Method},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {6030-6035},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202112},
        abstract = {The growing worldwide need for clean and renewable energy sources has significantly increased the adoption of photovoltaic (PV) systems. However, the nonlinear behaviour of PV modules and their sensitivity to environmental conditions such as solar irradiance and temperature create major difficulties in extracting maximum available power. As a result, Maximum Power Point Tracking (MPPT) techniques play a vital role in improving the efficiency and performance of PV energy conversion systems. Traditional MPPT methods, including the Incremental Conductance (INC) algorithm, are widely used due to their simple implementation, stable operation, and acceptable tracking performance under uniform environmental conditions. However, these conventional techniques often experience reduced effectiveness during rapidly changing weather conditions and partial shading situations because of increased oscillations around the maximum power point and slower response characteristics. To address these challenges, soft computing techniques, particularly Artificial Neural Networks (ANN), have gained attention as intelligent alternatives capable of learning complex nonlinear behaviours and providing adaptive control strategies. This work introduces a soft-computing-based hybrid MPPT controller that combines the forecasting capability of ANN with the reliability of the INC method. In the proposed system, the ANN is trained using extensive PV data, including voltage, current, irradiance, and temperature, to estimate the optimal duty cycle corresponding to the maximum power point. During real-time operation, the ANN acts as the primary estimator, enabling fast convergence and reducing steady-state oscillations. Meanwhile, the INC algorithm serves as a secondary correction mechanism to fine-tune the operating point whenever sudden or unexpected environmental variations cause deviations in the ANN prediction.},
        keywords = {Maximum Power Point Tracking (MPPT), Artificial Neutral Networks (ANN), Incremental Conductance (INC), Soft Computing, PV system, Solar Energy, Hybrid MPPT Controller, DC-DC converter, Intelligent Control Techniques.},
        month = {May},
        }