This paper investigates dynamic voltage restorers (DVRs) as a solution to voltage-based power quality issues. A control algorithm based on a Multilayer Gamma Filter is proposed to compensate for voltage sag, voltage swell, distortions, and phase voltage discrepancies caused by various factors in series-connected devices. To improve power quality, the proposed Multilayer Gamma Filter-based control algorithm is used for the compensator. In several ways, the Multilayer Gamma Filter outperforms traditional filter designs. To begin with, it is adaptive, which means it can change its parameters in response to various types of disturbances. Second, it has a lower computational complexity than other filters, making it a more efficient option for real-time applications. Finally, it has a high accuracy in estimating input signal parameters and a fast transient response. The system performs satisfactorily even under less-than-ideal conditions with wide voltage and load variations. The control algorithm employs two proportional integral (PI) gains to maintain the dc link voltage and terminal voltage stable. The gains of the PI controllers are critical for estimating reference load voltages correctly. The salp swarm optimization algorithm is used to optimize DVR gains. The salp swarm optimization algorithm has been demonstrated to be effective in PI tuning, providing faster convergence and improved performance over traditional tuning methods. The control system can achieve better regulation of the process variable and overall system performance by optimizing the PI gains. The optimized gains are applied to a fourth-order multilayer gamma filter to extract reference load voltages. To extract reference load voltages, the optimized gains are applied to a fourth-order multilayer gamma filter.


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    Titel :

    Control of DVR Using Multilayer Gamma Filter Under Non-Ideal Grid Conditions


    Beteiligte:


    Erscheinungsdatum :

    2023-08-09


    Format / Umfang :

    651109 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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