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Open AccessArticle

Supertwisting Sliding Mode Algorithm Based Nonlinear MPPT Control for a Solar PV System with Artificial Neural Networks Based Reference Generation

School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
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Energies 2020, 13(14), 3695; https://doi.org/10.3390/en13143695
Received: 1 May 2020 / Revised: 11 June 2020 / Accepted: 26 June 2020 / Published: 17 July 2020
(This article belongs to the Section Solar Energy and Photovoltaic Systems)
The problem of extracting maximum power from a photovoltaic (PV) system with negligible power loss is concerned with the power generating capability of the PV array and nature of the output load. Changing weather conditions and nonlinear behavior of PV systems pose a challenge in tracking of varying maximum power point. A robust nonlinear controller is required to ensure maximum power point tracking (MPPT) by handling nonlinearities of a system and making it robust against changing environmental conditions. Sliding mode controller is robust against disturbances, model uncertainties and parametric variations. It depicts undesirable phenomenon like chattering, inherent in it causing power and heat losses. In this paper, a supertwisting sliding mode algorithm based nonlinear robust controller has been designed for MPPT of a PV system which not only removes the chattering but also enhances the overall system’s dynamic response. Moreover, supertwisting sliding mode controller is robust against changing environmental conditions like change in temperature and irradiance. Noninverting DC-DC Buck-Boost converter has been used as an interface between source and the load. The efficiency of MPPT of a PV system depends upon the accuracy of reference for peak power voltage, therefore an efficient mechanism for reference generation has also been proposed in this work. The reference for peak power voltage has been generated by using a trained artificial neural network, which is to be tracked by proposed nonlinear controllers. Sliding mode controller (SMC) and synergetic controllers have also been designed for MPPT of a PV system in order to compare them with supertwisting sliding mode controller (ST-SMC). Global asymptotic stability of the system has been ensured by using Lyapunov stability criterion. The performance of the proposed nonlinear controllers has been validated in MATLAB/Simulink ODE 45 environment. ST-SMC has also been compared with recently proposed integral backstepping controller and other conventional MPPT controllers given in the literature. The simulation results show the better performance of ST-SMC in terms of best dynamic response and robustness. View Full-Text
Keywords: artificial neural networks (ANN); sliding mode controller (SMC); supertwisting sliding mode controller (ST-SMC); Lyapunov stability artificial neural networks (ANN); sliding mode controller (SMC); supertwisting sliding mode controller (ST-SMC); Lyapunov stability
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MDPI and ACS Style

Ahmed, S.; Muhammad Adil, H.M.; Ahmad, I.; Azeem, M.K.; e Huma, Z.; Abbas Khan, S. Supertwisting Sliding Mode Algorithm Based Nonlinear MPPT Control for a Solar PV System with Artificial Neural Networks Based Reference Generation. Energies 2020, 13, 3695. https://doi.org/10.3390/en13143695

AMA Style

Ahmed S, Muhammad Adil HM, Ahmad I, Azeem MK, e Huma Z, Abbas Khan S. Supertwisting Sliding Mode Algorithm Based Nonlinear MPPT Control for a Solar PV System with Artificial Neural Networks Based Reference Generation. Energies. 2020; 13(14):3695. https://doi.org/10.3390/en13143695

Chicago/Turabian Style

Ahmed, Shahzad; Muhammad Adil, Hafiz M.; Ahmad, Iftikhar; Azeem, Muhammad K.; e Huma, Zil; Abbas Khan, Safdar. 2020. "Supertwisting Sliding Mode Algorithm Based Nonlinear MPPT Control for a Solar PV System with Artificial Neural Networks Based Reference Generation" Energies 13, no. 14: 3695. https://doi.org/10.3390/en13143695

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