Analysis of Electrical Power Quality Disturbances Based on Empirical Mode Decomposition and Statistical Parameters

Abstract

Power quality becomes a severe problem due to the increasing use of nonlinear electrical loads, complex electric power systems on smart grids, inverters in renewable power plants, and electronic control equipment. Power quality problems include variations in voltage or current such as sag, swell, flicker, spike, overvoltage, undervoltage, interruption, transient, harmonics, and frequency fluctuations. Research on power quality disturbances mostly applies signal processing and transformation methods such as Fast Fourier transform, S-transform, and Wavelet. In this paper, we use empirical mode decomposition methods and statistical parameters to analyze power quality disturbance signals. It gives more detailed characteristics of power quality disturbances. We conducted a six-step analysis to get a percentage of each power quality disturbance signal. The developed method provides a preliminary description of the power quality characteristics from the percentage values of RMS, range, and energy levels at the first IMF. The positive percentage indicates the existence of power quality disturbances contains flicker and swell, while negative indicates contain harmonics, sag, and transient.

Development of empirical mode decomposition based neural network for power quality disturbances classification

“…Another shortcoming associated with these studies is the classification of the number of disturbances as few and not multi. This research is a continuation of the previous discussion on EMD-based PQ disturbances analysis and statistical parameters [23]. The proposed novelties are:…”

“…The Hilbert Huang Transform (HHT), which consists of two processes, is one of the tools for analyzing non-stationary complex waveforms with excellent time resolution [23]. First, the signal is decomposed into IMFs using EMD process, which has a significant effect on the instantaneous amplitude, frequency, and phase of the analyzed signal components.…”

“…Scale separation is defined as the minimum or maximum distance between two successive local extremes. The EMD algorithm is shown below [23]:…”