IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i14p3695-d386255.html
   My bibliography  Save this article

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

Author

Listed:
  • Shahzad Ahmed

    (School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Hafiz Mian Muhammad Adil

    (School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Iftikhar Ahmad

    (School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Muhammad Kashif Azeem

    (School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Zil e Huma

    (School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Safdar Abbas Khan

    (School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

Abstract

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.

Suggested Citation

  • Shahzad Ahmed & Hafiz Mian Muhammad Adil & Iftikhar Ahmad & Muhammad Kashif Azeem & Zil e Huma & Safdar Abbas Khan, 2020. "Supertwisting Sliding Mode Algorithm Based Nonlinear MPPT Control for a Solar PV System with Artificial Neural Networks Based Reference Generation," Energies, MDPI, vol. 13(14), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3695-:d:386255
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/14/3695/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/14/3695/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Kamran Ali & Laiq Khan & Qudrat Khan & Shafaat Ullah & Saghir Ahmad & Sidra Mumtaz & Fazal Wahab Karam & Naghmash, 2019. "Robust Integral Backstepping Based Nonlinear MPPT Control for a PV System," Energies, MDPI, vol. 12(16), pages 1-20, August.
    3. Danandeh, M.A. & Mousavi G., S.M., 2018. "Comparative and comprehensive review of maximum power point tracking methods for PV cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2743-2767.
    4. Somashree Pathy & C. Subramani & R. Sridhar & T. M. Thamizh Thentral & Sanjeevikumar Padmanaban, 2019. "Nature-Inspired MPPT Algorithms for Partially Shaded PV Systems: A Comparative Study," Energies, MDPI, vol. 12(8), pages 1-21, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sy Ngo & Chian-Song Chiu & Thanh-Dong Ngo, 2022. "A Novel Horse Racing Algorithm Based MPPT Control for Standalone PV Power Systems," Energies, MDPI, vol. 15(20), pages 1-18, October.
    2. 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.
    3. Mohamed Ali Zdiri & Tawfik Guesmi & Badr M. Alshammari & Khalid Alqunun & Abdulaziz Almalaq & Fatma Ben Salem & Hsan Hadj Abdallah & Ahmed Toumi, 2022. "Design and Analysis of Sliding-Mode Artificial Neural Network Control Strategy for Hybrid PV-Battery-Supercapacitor System," Energies, MDPI, vol. 15(11), pages 1-20, June.
    4. Hassen Moussa & Saber Krim & Hichem Kesraoui & Majdi Mansouri & Mohamed Faouzi Mimouni, 2024. "Loss Model Control for Efficiency Optimization and Advanced Sliding Mode Controllers with Chattering Attenuation for Five-Phase Induction Motor Drive," Energies, MDPI, vol. 17(16), pages 1-37, August.
    5. Fateh Mehazzem & Maina André & Rudy Calif, 2022. "Efficient Output Photovoltaic Power Prediction Based on MPPT Fuzzy Logic Technique and Solar Spatio-Temporal Forecasting Approach in a Tropical Insular Region," Energies, MDPI, vol. 15(22), pages 1-21, November.
    6. Pawel Latosinski & Andrzej Bartoszewicz, 2023. "Sliding Mode Controllers in Energy Systems and Other Applications," Energies, MDPI, vol. 16(3), pages 1-4, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Adibpour, S. & Raisi, A. & Ghasemi, B. & Sajadi, A.R. & Rosengarten, G., 2021. "Experimental investigation of the performance of a sun tracking photovoltaic panel with Phase Change Material," Renewable Energy, Elsevier, vol. 165(P1), pages 321-333.
    2. Subramanian Vasantharaj & Vairavasundaram Indragandhi & Vairavasundaram Subramaniyaswamy & Yuvaraja Teekaraman & Ramya Kuppusamy & Srete Nikolovski, 2021. "Efficient Control of DC Microgrid with Hybrid PV—Fuel Cell and Energy Storage Systems," Energies, MDPI, vol. 14(11), pages 1-18, June.
    3. Yao, Ganzhou & Luo, Zirong & Lu, Zhongyue & Wang, Mangkuan & Shang, Jianzhong & Guerrerob, Josep M., 2023. "Unlocking the potential of wave energy conversion: A comprehensive evaluation of advanced maximum power point tracking techniques and hybrid strategies for sustainable energy harvesting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    4. Rezk, Hegazy & AL-Oran, Mazen & Gomaa, Mohamed R. & Tolba, Mohamed A. & Fathy, Ahmed & Abdelkareem, Mohammad Ali & Olabi, A.G. & El-Sayed, Abou Hashema M., 2019. "A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    5. Waleed Al Abri & Rashid Al Abri & Hassan Yousef & Amer Al-Hinai, 2021. "A Simple Method for Detecting Partial Shading in PV Systems," Energies, MDPI, vol. 14(16), pages 1-12, August.
    6. Yu-Pei Huang & Cheng-En Ye & Xiang Chen, 2018. "A Modified Firefly Algorithm with Rapid Response Maximum Power Point Tracking for Photovoltaic Systems under Partial Shading Conditions," Energies, MDPI, vol. 11(9), pages 1-33, August.
    7. Diogo Cabral & Abolfazl Hayati & João Gomes & Hossein Afzali Gorouh & Pouriya Nasseriyan & Mazyar Salmanzadeh, 2023. "Experimental Electrical Assessment Evaluation of a Vertical n-PERT Half-Size Bifacial Solar Cell String Receiver on a Parabolic Trough Solar Collector," Energies, MDPI, vol. 16(4), pages 1-21, February.
    8. Mehdi Seyedmahmoudian & Gokul Sidarth Thirunavukkarasu & Elmira Jamei & Tey Kok Soon & Ben Horan & Saad Mekhilef & Alex Stojcevski, 2020. "A Sustainable Distributed Building Integrated Photo-Voltaic System Architecture with a Single Radial Movement Optimization Based MPPT Controller," Sustainability, MDPI, vol. 12(16), pages 1-21, August.
    9. Omer Saleem & Shehryaar Ali & Jamshed Iqbal, 2023. "Robust MPPT Control of Stand-Alone Photovoltaic Systems via Adaptive Self-Adjusting Fractional Order PID Controller," Energies, MDPI, vol. 16(13), pages 1-20, June.
    10. Zaheer Alam & Qudrat Khan & Laiq Khan & Safeer Ullah & Syed Abdul Mannan Kirmani & Abdullah A. Algethami, 2022. "Certainty-Equivalence-Based Sensorless Robust Sliding Mode Control for Maximum Power Extraction of an Uncertain Photovoltaic System," Energies, MDPI, vol. 15(6), pages 1-17, March.
    11. Ali Abedaljabar Al-Samawi & Hafedh Trabelsi, 2022. "New Nine-Level Cascade Multilevel Inverter with a Minimum Number of Switches for PV Systems," Energies, MDPI, vol. 15(16), pages 1-25, August.
    12. 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.
    13. Jong-Chan Kim & Jun-Ho Huh & Jae-Sub Ko, 2020. "Optimization Design and Test Bed of Fuzzy Control Rule Base for PV System MPPT in Micro Grid," Sustainability, MDPI, vol. 12(9), pages 1-25, May.
    14. Lu, Zhen & Huang, Yuewu & Zhao, Yonggang, 2023. "Elastocaloric cooler for waste heat recovery from perovskite solar cell with electricity and cooling production," Renewable Energy, Elsevier, vol. 215(C).
    15. Eyal Amer & Alon Kuperman & Teuvo Suntio, 2019. "Direct Fixed-Step Maximum Power Point Tracking Algorithms with Adaptive Perturbation Frequency," Energies, MDPI, vol. 12(3), pages 1-16, January.
    16. Abdelilah Chalh & Aboubakr El Hammoumi & Saad Motahhir & Abdelaziz El Ghzizal & Umashankar Subramaniam & Aziz Derouich, 2020. "Trusted Simulation Using Proteus Model for a PV System: Test Case of an Improved HC MPPT Algorithm," Energies, MDPI, vol. 13(8), pages 1-12, April.
    17. Marco Balato & Carlo Petrarca, 2020. "The Impact of Reconfiguration on the Energy Performance of the Distributed Maximum Power Point Tracking Approach in PV Plants," Energies, MDPI, vol. 13(6), pages 1-19, March.
    18. Luigi Costanzo & Massimo Vitelli, 2019. "A Novel MPPT Technique for Single Stage Grid-Connected PV Systems: T4S," Energies, MDPI, vol. 12(23), pages 1-13, November.
    19. 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.
    20. Victor Andrean & Pei Cheng Chang & Kuo Lung Lian, 2018. "A Review and New Problems Discovery of Four Simple Decentralized Maximum Power Point Tracking Algorithms—Perturb and Observe, Incremental Conductance, Golden Section Search, and Newton’s Quadratic Int," Energies, MDPI, vol. 11(11), pages 1-25, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3695-:d:386255. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.