Research on Power Quality Disturbance Detection Method Based on Improved Ensemble Empirical Mode Decomposition

Abstract

With the increasing proportion of various unbalanced loads in the power grid, power quality is seriously challenged. It is of great significance to effectively detect, analyze, and evaluate the power quality problems. First, this paper introduces the current situation of power quality (PQ) disturbance detection methods. It summarizes that the current PQ disturbance detection methods include Wavelet Transform (WT), Hilbert–Huang Transform (HHT), and Ensemble Empirical Mode Decomposition (EEMD). EEMD has a better detection accuracy, but its running time is longer. Therefore, to reduce the running time of the EEMD algorithm, this paper proposed two improvements: increasing the screening threshold and selecting piecewise cubic Hermite interpolation polynomial fitting. At the same time, the mathematical models of transient power quality disturbance and harmonic were established for comparative verification. The experimental results showed that the improved Ensemble Empirical Mode Decomposition (IEEMD) algorithm greatly reduced the running time of the algorithm on the premise of ensuring the detection accuracy. Hence, the improvement of this paper is of great significance for the industrial application of the EEMD algorithm.

Compressed-Sensing-Based Time–Frequency Representation for Disturbance Characterization of Maglev On-Board Distribution Systems

“…where F −1 2D is the inverse 2-D Fourier transform matrix and is regarded as the compressed sensing matrix like A CS in (11). As a result, the target time-frequency distribution TF AOK is regarded as the high-dimension signal y to be recovered in this compressed sensing system.…”

“…result in transient or short-duration waveforms [6][7][8][9][10]. As a result, time-frequency representation has drawn extensive attention as a better solution for disturbance characterization and diagnosis of various distribution systems [11][12][13][14][15][16]. The target signal would be distributed on the time-frequency plane as a 2-dimensional (2-D) pattern, showing different frequency components varying with time [17].…”

Real Time Intelligent Detection of PQ Disturbances With Variational Mode Energy Features and Hybrid Optimized Light GBM Classifier

“…Since PQ signals possess lot of irregularity in terms of amplitude and frequency and often noise contaminated, it is necessary to implement such a signal processor that can act on non-stationary, non-linear and noise prone signals. In this context, mode decomposition techniques like, empirical made decomposition (EMD) [6], ensemble EMD (EEMD) [7], down-sampling EMD (DEMD) [8] are much more useful. But these methods are suffering two major drawbacks such as mode mixing and end effect when the signal nature is very complex.…”