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Hybrid Neural Network and Adaptive Terminal Sliding Mode MPPT Controller for Partially Shaded Standalone PV Systems

  • Research Article-Electrical Engineering
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Abstract

This research presents a new method for controlling the maximum power point tracking (MPPT) of solar photovoltaic (PV) systems that are partially shaded. The proposed approach uses a neural network and an adaptive terminal sliding mode controller (NN-ATSMC) to ensure that the PV system operates at optimal performance under uncertain conditions. The NN-ATSMC controller is applied to a DC/DC boost converter to drive the system to the maximum power point (MPP). This method ensures that the error will converge in finite time and the chattering effect will be minimized without losing robustness under various disturbances and load conditions. Simulation results show that the proposed NN-ATSMC controller performs better than other types of controllers existing in the literature, such as a sliding mode controller (SMC) and a conventional proportional-integral controller (CPI). For the validation of the proposed controller, control hardware-in-the-loop (C-HIL) experimental implementation has been carried out through Texas Instruments digital signal processor C2000. The experimental results show the viability of real-time implementation and verify the effectiveness of the proposed method, which ensures the low cost and stability of the standalone PV systems.

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Acknowledgements

The authors would like to acknowledge the support provided by the Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS) at the King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Kingdom of Saudi Arabia, through the funded project No. INRE2313. Dr. Abido acknowledges also the KACARE Energy Research and Innovation Center (ERIC), KFUPM.

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Authors and Affiliations

  1. Department of Electrical Engineering, College of Engendering and Physics, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia

    Abdullah Baraean, Mahmoud Kassas & Mohamed A. Abido

  2. Interdisciplinary Research Center of Renewable Energy and Power Systems (IRC-REPS), Research Institute, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia

    Mahmoud Kassas & Mohamed A. Abido

  3. Applied Research Center for Environment and Marine Studies, Research Institute, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia

    Md Shafiul Alam

  4. K.A.CARE Energy Research and Innovation Center, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia

    Mohamed A. Abido

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  1. Abdullah Baraean
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Correspondence to Mahmoud Kassas.

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Baraean, A., Kassas, M., Alam, M.S. et al. Hybrid Neural Network and Adaptive Terminal Sliding Mode MPPT Controller for Partially Shaded Standalone PV Systems. Arab J Sci Eng 48, 15527–15539 (2023). https://doi.org/10.1007/s13369-023-08179-9

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