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Adaptive Voltage Regulation Control Strategy in a Stand-Alone Islanded DC Microgrid based on distributed Wind / Photovoltaic / Diesel / Energy Storage Hybrid Energy Conversion System

Author

Listed:
  • Adel Elgammal

    (The University of Trinidad and Tobago UTT, Trinidad and Tobago)

  • Tagore Ramlal

    (The University of Trinidad and Tobago UTT, Trinidad and Tobago)

Abstract

An adaptive approach for optimal tuning of a SMC for an automated voltage regulator system is displayed in this study. The approach is centered on hybrid of the GA and MOPSA. In addition, unique objective functions for the controller's parameter optimization are suggested. The performance of the resulting perfect sliding mode controller is confirmed by comparing it to controllers adjusted using various techniques that have been published in the literature. The simulation outcomes indicate that controllers tuned with the projected MOPSO and GA algorithms outperform controllers tuned with existing methods. In addition, a comparison study is performed to select the best controller for use in AVR systems. The suggested algorithm's major benefit is a considerable boost in convergence speed. With step changes and step load modifications in input wind power, the system model with built-in intelligent controller is generated in MATLAB/SIMULINK. The benefits of the recommended intelligent control algorithm are confirmed by comparing the outcomes of the sliding mode controller and the projected MOPSO self-tuned controller. The findings show that the hybrid Wind/PV system's reactive power adjustment capabilities. When used in conjunction with BES, it is extremely successful in optimising the voltage profile although providing active energy to local load.

Suggested Citation

  • Adel Elgammal & Tagore Ramlal, 2021. "Adaptive Voltage Regulation Control Strategy in a Stand-Alone Islanded DC Microgrid based on distributed Wind / Photovoltaic / Diesel / Energy Storage Hybrid Energy Conversion System," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 5(4), pages 26-33, July.
  • Handle: RePEc:epw:ejece0:v:5:y:2021:i:4:id:19343
    DOI: 10.24018/ejece.2021.5.4.343
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