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Implementation of an ADALINE-Based Adaptive Control Strategy for an LCLC-PV-DSTATCOM in Distribution System for Power Quality Improvement

Author

Listed:
  • Soumya Mishra

    (Department of Electrical Engineering, MVJ College of Engineering, Bengaluru 560067, India)

  • Sreejith Rajashekaran

    (Department of Electrical Engineering, MVJ College of Engineering, Bengaluru 560067, India)

  • Pavan Kalyan Mohan

    (Department of Electrical Engineering, MVJ College of Engineering, Bengaluru 560067, India)

  • Spoorthi Mathad Lokesh

    (Department of Electrical Engineering, MVJ College of Engineering, Bengaluru 560067, India)

  • Hemalatha Jyothinagaravaishya Ganiga

    (Department of Electrical Engineering, MVJ College of Engineering, Bengaluru 560067, India)

  • Santanu Kumar Dash

    (TIFAC-CORE, Vellore Institute of Technology, Vellore 632014, India)

  • Michele Roccotelli

    (Department of Electrical and Information Engineering (DEI), Politecnico di Bari, Via Orabona, 4, 70125 Bari, Italy)

Abstract

This study investigated the problem of controlling a three-phase three-wire photovoltaic (PV)-type distribution static compensator (DSTATCOM). In order to model, simulate, and control the system, the MATLAB/SIMULINK tool was used. Different controllers were applied to create switching pulses for the IGBT-based voltage source converter (VSC) for the mitigation of various power quality issues in the PV-DSTATCOM. Traditional control algorithms guarantee faultless execution or outcomes only for a restricted range of operating situations due to their present design. Alternative regulators depend on more resilient neural network and fuzzy logic algorithms that may be programmed to operate in a variety of settings. In this study, an adaptive linear neural network (ADALINE) was proposed to solve the control problem more efficiently than the existing methods. The ADALINE method was simulated and the results were compared with the results of the synchronous reference frame theory (SRFT), improved linear sinusoidal tracer (ILST), and backpropagation (BP) algorithms. The simulation results showed that the proposed ADALINE method outperformed the compared algorithms. In addition, the total harmonic distortions (THDs) of the source current were estimated under ideal grid voltage conditions based on IEEE-929 and IEEE-519 guidelines.

Suggested Citation

  • Soumya Mishra & Sreejith Rajashekaran & Pavan Kalyan Mohan & Spoorthi Mathad Lokesh & Hemalatha Jyothinagaravaishya Ganiga & Santanu Kumar Dash & Michele Roccotelli, 2022. "Implementation of an ADALINE-Based Adaptive Control Strategy for an LCLC-PV-DSTATCOM in Distribution System for Power Quality Improvement," Energies, MDPI, vol. 16(1), pages 1-22, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:323-:d:1017544
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    References listed on IDEAS

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    1. Santanu Kumar Dash & Suprava Chakraborty & Michele Roccotelli & Umesh Kumar Sahu, 2022. "Hydrogen Fuel for Future Mobility: Challenges and Future Aspects," Sustainability, MDPI, vol. 14(14), pages 1-22, July.
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