Abstract

This study addresses the problem of controlling series active power filters for voltage sag compensation. Indeed, we can control this kind of filter to generate a voltage series that compensates the grid voltage sag in order to protect the sensitive loads against this perturbation. This study is aimed at seeking a control strategy that meets the main control objective, which is the compensation of grid voltage sags and this by considering the following technical constraints: (i) the nonlinearity of the system dynamics, (ii) the high dimension of the system model, and (iii) the inaccessibility of some system variables to measurements. To meet the main control objective, we propose a nonlinear controller that is designed based on the system nonlinear model, using the backstepping technique. This controller involves a nonlinear regulator and a grid observer. The former copes with the compensation issue. The observer provides online the grid voltage estimations. In addition to a theoretical analysis of the control system, the performances of the proposed controller are evaluated by simulation using MATLAB/Simulink.

Highlights

  • The voltage wave of the power grid can be affected by voltage sag, which is a sudden decrease in the magnitude of the electrical voltage below a lower threshold of the nominal range

  • We are interested in single-phase halfbridge series active power filters connected between the power grid and the sensitive loads

  • We study the problem of controlling a single-phase half-bridge series active power filter operating in the presence of sensitive loads

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Summary

Introduction

The voltage wave of the power grid can be affected by voltage sag, which is a sudden decrease in the magnitude of the electrical voltage below a lower threshold of the nominal range. We are interested in single-phase halfbridge series active power filters connected between the power grid and the sensitive loads (e.g., consumer electronics, variable frequency motor drives, computer numerical control equipment, and automated systems and processes). We find the linear controls [6,7,8,9] With these control approaches in [6,7,8,9], the optimal performances are not guaranteed in a wide variation range because of the presence of controlled system nonlinearity.

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