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Practical Implementation of the Backstepping Sliding Mode Controller MPPT for a PV-Storage Application

Author

Listed:
  • Marwen Bjaoui

    (LANSER Laboratory/CRTEn B.P.95 Hammam-Lif, Tunis 2050, Tunisia)

  • Brahim Khiari

    (LANSER Laboratory/CRTEn B.P.95 Hammam-Lif, Tunis 2050, Tunisia)

  • Ridha Benadli

    (LANSER Laboratory/CRTEn B.P.95 Hammam-Lif, Tunis 2050, Tunisia)

  • Mouad Memni

    (LANSER Laboratory/CRTEn B.P.95 Hammam-Lif, Tunis 2050, Tunisia)

  • Anis Sellami

    (Research unit: LISIER, National Higher Engineering School of Tunis, Tunis 2050, Tunisia)

Abstract

This study presents a design and an implementation of a robust Maximum Power Point Tracking (MPPT) for a stand-alone photovoltaic (PV) system with battery storage. A new control scheme is applied for the boost converter based on the combination of the adaptive perturb and observe fuzzy logic controller (P&O-FLC) MPPT technique and the backstepping sliding mode control (BS-SMC) approach. The MPPT controller design was used to accurately track the PV operating point to its maximum power point (MPP) under changing climatic conditions. The presented MPPT based on the P&O-FLC technique generates the reference PV voltage and then a cascade control loop type, based on the BS-SMC approach is used. The aims of this approach are applied to regulate the inductor current and then the PV voltage to its reference values. In order to reduce system costs and complexity, a high gain observer (HGO) was designed, based on the model of the PV system, to estimate online the real value of the boost converter’s inductor current. The performance and the robustness of the BS-SMC approach are evaluated using a comparative simulation with a conventional proportional integral (PI) controller implemented in the MATLAB/Simulink environment. The obtained results demonstrate that the proposed approach not only provides a near-perfect tracking performance (dynamic response, overshoot, steady-state error), but also offers greater robustness and stability than the conventional PI controller. Experimental results fitted with dSPACE software reveal that the PV module could reach the MPP and achieve the performance and robustness of the designed BS-SMC MPPT controller.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3539-:d:267542
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    References listed on IDEAS

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    1. Yilmaz, Unal & Kircay, Ali & Borekci, Selim, 2018. "PV system fuzzy logic MPPT method and PI control as a charge controller," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 994-1001.
    2. Abdul Rehman Yasin & Muhammad Ashraf & Aamer Iqbal Bhatti, 2019. "A Novel Filter Extracted Equivalent Control Based Fixed Frequency Sliding Mode Approach for Power Electronic Converters," Energies, MDPI, vol. 12(5), pages 1-14, March.
    3. Spanos, Constantine & Turney, Damon E. & Fthenakis, Vasilis, 2015. "Life-cycle analysis of flow-assisted nickel zinc-, manganese dioxide-, and valve-regulated lead-acid batteries designed for demand-charge reduction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 478-494.
    4. Mohapatra, Alivarani & Nayak, Byamakesh & Das, Priti & Mohanty, Kanungo Barada, 2017. "A review on MPPT techniques of PV system under partial shading condition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 854-867.
    5. Messalti, Sabir & Harrag, Abdelghani & Loukriz, Abdelhamid, 2017. "A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 221-233.
    6. Julie Viloria-Porto & Carlos Robles-Algarín & Diego Restrepo-Leal, 2018. "A Novel Approach for an MPPT Controller Based on the ADALINE Network Trained with the RTRL Algorithm," Energies, MDPI, vol. 11(12), pages 1-17, December.
    7. Abu Eldahab, Yasser E. & Saad, Naggar H. & Zekry, Abdalhalim, 2016. "Enhancing the design of battery charging controllers for photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 646-655.
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    Cited by:

    1. Miaomiao Ma & Xiangjie Liu & Kwang Y. Lee, 2020. "Maximum Power Point Tracking and Voltage Regulation of Two-Stage Grid-Tied PV System Based on Model Predictive Control," Energies, MDPI, vol. 13(6), pages 1-16, March.
    2. Lu Liu & Yun Zeng, 2023. "Intelligent ISSA-Based Non-Singular Terminal Sliding-Mode Control of DC–DC Boost Converter Feeding a Constant Power Load System," Energies, MDPI, vol. 16(13), pages 1-23, June.
    3. 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.
    4. Waleed Al Abri & Rashid Al Abri & Hassan Yousef & Amer Al-Hinai, 2022. "A Maximum Power Point Tracker Using the Bald Eagle Search Technique for Grid-Connected Photovoltaic Systems," Energies, MDPI, vol. 15(23), pages 1-16, December.
    5. Sameh Mahjoub & Larbi Chrifi-Alaoui & Saïd Drid & Nabil Derbel, 2023. "Control and Implementation of an Energy Management Strategy for a PV–Wind–Battery Microgrid Based on an Intelligent Prediction Algorithm of Energy Production," Energies, MDPI, vol. 16(4), pages 1-26, February.

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