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Chattering Free Adaptive Sliding Mode Controller for Photovoltaic Panels with Maximum Power Point Tracking

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  • Hina Gohar Ali

    (Department Telecommunications and Systems Engineering, School of Engineering, Universitat Autonoma de Barcelona, Bellaterra, 08193 Cerdanyola del Vallés, Barcelona, Spain
    School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Ramon Vilanova Arbos

    (Department Telecommunications and Systems Engineering, School of Engineering, Universitat Autonoma de Barcelona, Bellaterra, 08193 Cerdanyola del Vallés, Barcelona, Spain)

Abstract

Photovoltaic system is utilized to generate energy that relies upon the ecological conditions, for example, temperature, irradiance, and the load associated with it. Considering the non-linear component of photovoltaic (PV) array and the issue of low effectiveness because of the variable natural conditions, the Maximum Power Point Tracking (MPPT) method is required to extract the maximum power from the PV system. The adopted control is executed utilizing an Adaptive Sliding Mode Controller (ASMC) and the enhancement is actualized utilizing an Improved Pattern Search Method (IPSM). This work employs IPSM based optimization approach in order to command the underlying ASMC controller. The upper level decision determines the sliding surface for the adaptive controller. As a non-linear strategy, the stability of the adaptive controller is guaranteed by conducting a Liapunov analysis. On the practical side, MATLAB/Simulink is used as simulator for the controller implementation and coupling with PSIM in order to connect it with the PV system object of control. The simulation results validate that the proposed controller effectively improves the voltage tracking, system power with reduced chattering effect and steady-state error. The performance of the proposed control architectures is validated by comparing the proposals with that of the well-known and widely used Proportional Integral Derivative (PID) controller. That operated as a lower level controller for a Perturb & Observe (P&O) and Particle Swarm Optimization (PSO).

Suggested Citation

  • Hina Gohar Ali & Ramon Vilanova Arbos, 2020. "Chattering Free Adaptive Sliding Mode Controller for Photovoltaic Panels with Maximum Power Point Tracking," Energies, MDPI, vol. 13(21), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5678-:d:437443
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    References listed on IDEAS

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    1. Amjad Ali & K. Almutairi & Muhammad Zeeshan Malik & Kashif Irshad & Vineet Tirth & Salem Algarni & Md. Hasan Zahir & Saiful Islam & Md Shafiullah & Neeraj Kumar Shukla, 2020. "Review of Online and Soft Computing Maximum Power Point Tracking Techniques under Non-Uniform Solar Irradiation Conditions," Energies, MDPI, vol. 13(12), pages 1-37, June.
    2. Salam, Zainal & Ahmed, Jubaer & Merugu, Benny S., 2013. "The application of soft computing methods for MPPT of PV system: A technological and status review," Applied Energy, Elsevier, vol. 107(C), pages 135-148.
    3. Ram, J. Prasanth & Babu, T. Sudhakar & Rajasekar, N., 2017. "A comprehensive review on solar PV maximum power point tracking techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 826-847.
    4. Bahgat, A.B.G. & Helwa, N.H. & Ahmad, G.E. & El Shenawy, E.T., 2005. "Maximum power point traking controller for PV systems using neural networks," Renewable Energy, Elsevier, vol. 30(8), pages 1257-1268.
    5. Karami, Nabil & Moubayed, Nazih & Outbib, Rachid, 2017. "General review and classification of different MPPT Techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 1-18.
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    Cited by:

    1. Zahra Bel Hadj Salah & Saber Krim & Mohamed Ali Hajjaji & Badr M. Alshammari & Khalid Alqunun & Ahmed Alzamil & Tawfik Guesmi, 2023. "A New Efficient Cuckoo Search MPPT Algorithm Based on a Super-Twisting Sliding Mode Controller for Partially Shaded Standalone Photovoltaic System," Sustainability, MDPI, vol. 15(12), pages 1-38, June.
    2. Gianfranco Di Lorenzo & Erika Stracqualursi & Rodolfo Araneo, 2022. "The Journey Towards the Energy Transition: Perspectives from the International Conference on Environment and Electrical Engineering (EEEIC)," Energies, MDPI, vol. 15(18), pages 1-5, September.
    3. Amjad Ali & Kashif Irshad & Mohammad Farhan Khan & Md Moinul Hossain & Ibrahim N. A. Al-Duais & Muhammad Zeeshan Malik, 2021. "Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review," Sustainability, MDPI, vol. 13(19), pages 1-26, September.

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