Automatic Expansion of Voltage Signals Using Empirical Mode Decomposition for Voltage Sag Detection

Abstract

Voltage sag is one of the most harmful power quality issues. In practical engineering, harmonic and noise interference problems will bring big challenges to the analysis of voltage signals. These disturbances can easily lead to a delay or even false detection of the voltage signal. To address this problem, adaptive processing and diagnosis methods of the voltage signal, such as the Empirical Mode Decomposition (EMD) and the Variational Mode Decomposition (VMD), have become a research hotspot. In order to overcome the interference of voltage harmonic and achieve rapid voltage sag detection, this paper first analyzes the performance of EMD and VMD in decomposing voltage sag signals. Then, a tailored EMD-based adaptive voltage signal expansion method for real-time voltage sag detection is proposed. The sampling voltage signal is automatically expanded using the real-time voltage signal to achieve the rapid detection of voltage sag under complex operational environments. Numerical results demonstrate that the proposed method can detect the voltage sag within 1 millisecond.

Keywords

• Power quality
• voltage sag
• tailored empirical mode decomposition
• Hilbert transform