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An Improved Sliding Mode Controller for MPP Tracking of Photovoltaics

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  • Fatemeh Jamshidi

    (Department of Electrical Engineering, Faculty of Engineering, Fasa University, Fasa 74616-86131, Iran)

  • Mohammad Reza Salehizadeh

    (Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran)

  • Reza Yazdani

    (Faculty of Electrical Engineering, Pasargad Higher Education Institute, Shiraz 71769-84578, Iran)

  • Brian Azzopardi

    (MCAST Energy Research Group, Institute of Engineering and Transport, Malta College of Arts, Science and Technology, 9032 Paola, Malta)

  • Vibhu Jately

    (Department of Electrical and Electronics Engineering, School of Engineering, University of Petroleum and Energy Studies, Dehradun 248001, India)

Abstract

Maximum power point tracking (MPPT) through an effective control strategy increases the efficiency of solar panels under rapidly changing atmospheric conditions. Due to the nonlinearity of the I–V characteristics of the PV module, the Sliding Mode Controller (SMC) is considered one of the commonly used control approaches for MPPT in the literature. This paper proposed a Backstepping SMC (BSMC) method that ensures system stability using Lyapunov criteria. A fuzzy inference system replaces the saturation function, and a modified SMC is used for MPPT to ensure smooth behavior. The proposed Fuzzy BSMC (FBSMC) parameters are optimized using a Particle Swarm Optimization (PSO) approach. The proposed controller is tested through various case studies on account of MPP’s dependence on temperature and solar radiation. The controller performance is assessed in partial shading conditions as well. The simulation results show that less settling time, a small error, and enhanced power extraction capability are achieved by applying the PSO-based FBSMC approach compared to the conventional BSMC- and ABC-based PI control presented in previous research in different scenarios. Moreover, the proposed approach provides faster adaptation to temperature and solar radiation variation, ensuring faster convergence to the MPP. Finally, the robustness of the proposed controller is validated by providing variation within the system components. The result of the proposed controller clearly indicates the lowest value of RMSE measured between PV voltage and the reference voltage, as well as the RMSE between PV power and maximum power. The results also show that the proposed MPPT controller exhibits the highest dynamic efficiency and mean power.

Suggested Citation

  • Fatemeh Jamshidi & Mohammad Reza Salehizadeh & Reza Yazdani & Brian Azzopardi & Vibhu Jately, 2023. "An Improved Sliding Mode Controller for MPP Tracking of Photovoltaics," Energies, MDPI, vol. 16(5), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2473-:d:1088349
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    References listed on IDEAS

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    1. Jishu Mary Gomez & Prabhakar Karthikeyan Shanmugam, 2022. "Flexible Power Point Tracking Using a Neural Network for Power Reserve Control in a Grid-Connected PV System," Energies, MDPI, vol. 15(21), pages 1-17, November.
    2. Lilia Tightiz & Saeedeh Mansouri & Farhad Zishan & Joon Yoo & Nima Shafaghatian, 2022. "Maximum Power Point Tracking for Photovoltaic Systems Operating under Partially Shaded Conditions Using SALP Swarm Algorithm," Energies, MDPI, vol. 15(21), pages 1-17, November.
    3. Azzopardi, B. & Mutale, J., 2010. "Life cycle analysis for future photovoltaic systems using hybrid solar cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 1130-1134, April.
    4. Raugei, Marco & Leccisi, Enrica & Azzopardi, Brian & Jones, Christopher & Gilbert, Paul & Zhang, Lingxi & Zhou, Yutian & Mander, Sarah & Mancarella, Pierluigi, 2018. "A multi-disciplinary analysis of UK grid mix scenarios with large-scale PV deployment," Energy Policy, Elsevier, vol. 114(C), pages 51-62.
    5. Marwen Bjaoui & Brahim Khiari & Ridha Benadli & Mouad Memni & Anis Sellami, 2019. "Practical Implementation of the Backstepping Sliding Mode Controller MPPT for a PV-Storage Application," Energies, MDPI, vol. 12(18), pages 1-22, September.
    6. Jong-Chan Kim & Jun-Ho Huh & Jae-Sub Ko, 2019. "Improvement of MPPT Control Performance Using Fuzzy Control and VGPI in the PV System for Micro Grid," Sustainability, MDPI, vol. 11(21), pages 1-27, October.
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