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

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 585 ◽  
Author(s):  
He Wang ◽  
Jinhao Liu ◽  
Shuqi Luo ◽  
Xiangbo Xu
Keyword(s):  
Power Quality ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Interpolation Polynomial ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Running Time ◽  
Mode Decomposition ◽  
Power Quality Disturbance ◽  
Disturbance Detection

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.

Identification of Power Quality Disturbances Based on EEMD and TEO

2013 ◽  
Vol 433-435 ◽  
pp. 469-476 ◽  
Author(s):  
Song Jun Wang ◽  
Qing Fen Liao ◽  
Di Chen Liu ◽  
Yu Tian Zhou ◽  
Bin Kun Xu ◽  
...  
Keyword(s):  
Power Quality ◽  
Empirical Mode Decomposition ◽  
Energy Operator ◽  
Ensemble Empirical Mode Decomposition ◽  
Good Time ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Power Quality Disturbance ◽  
Stationary Signals ◽  
Power Quality Disturbances

The empirical mode decomposition (EMD) is a good time-frequency analysis method, which can deal with nonlinear and non-stationary signals. Aiming at improving modal aliasing problem brought by the traditional EMD, white noise is introduced into the improved aided analysis algorithm namely ensemble empirical mode decomposition (EEMD), instantaneous amplitude and frequency can be obtained by using teager energy operator (TEO), which is adopted to identify the type of power quality disturbance. The anti-aliasing of EEMD and real-time detection of TEO are verified by the signal simulation in Matlab. Simulation and experimental results show that the proposed algorithm can detect and locate power quality disturbances accurately and quickly, with excellent detection effects.

scholarly journals Non-Contact Geomagnetic Detection Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Teager Energy Operator

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 309 ◽  
Author(s):  
Tao Zhang ◽  
Xinhua Wang ◽  
Yingchun Chen ◽  
Zia Ullah ◽  
Haiyang Ju ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Energy Operator ◽  
Side Lobe ◽  
Ensemble Empirical Mode Decomposition ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Teager Energy Operator ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Teager Energy

During the non-contact geomagnetic detection of pipeline defects, measured signals generally contain noise, which reduces detection efficiency. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) has recently emerged as a signal filtering method, but its filtering performance is influenced by two parameters: the amplitude of added noise and the number of ensemble trials. To solve this issue and improve detection accuracy and distinguishability, a detection method based on improved CEEMDAN (ICEEDMAN) and the Teager energy operator (TEO) is proposed. The magnetic detection signal was first decomposed into a series of intrinsic mode functions (IMFs) by CEEMDAN with initial parameters. Signal IMFs were then distinguished using the Hurst exponent to reconstruct the preliminary filtered signal, and its maximum value (except the zero point) of the normalized autocorrelation function was defined as salp swarm algorithm (SSA) fitness. The optimal parameters that maximize fitness were found by SSA iterations, and their corresponding filtered signal was obtained. Finally, the gradient calculation and TEO were carried out to complete non-contact geomagnetic detection. The results of the simulated signal based on magnetic dipole under a noisy environment and field testing prove that ICEEMDAN denoising has better filtering performance than conventional CEEMDAN denoising methods, and ICEEMDAN-TEO has obvious advantages compared to other detection methods in the aspects of location error, peak side-lobe ratio, and integrated side-lobe ratio.

Application of Bidimensional Empirical Mode Decomposition to Medical Liquid Opacity Detection

2011 ◽  
Vol 128-129 ◽  
pp. 530-533
Author(s):  
Jian Wan ◽  
Yuan Peng Diao ◽  
Dong Mei Yan ◽  
Qiang Guo ◽  
Zhen Shen Qu
Keyword(s):  
Edge Detection ◽  
Empirical Mode Decomposition ◽  
External Environment ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Edge Detection Algorithm ◽  
Mode Decomposition ◽  
Simulation Results ◽  
Bidimensional Empirical Mode Decomposition

A Robert operator edge detection algorithm based on Bidimensional Empirical Mode Decomposition (BEMD) to detect medical liquid opacity is proposed. This method can effectively resolve the problem that traditional Robert operator edge detection can be easily effected by noise, and it also has certain effects on restraining external environment influence. The simulation results show that, compare with traditional medical liquid opacity detection methods, the proposed method could achieve higher detection accuracy, and has a certain theory and application value.

A Method of Real-Time Tsunami Detection Using Ensemble Empirical Mode Decomposition

2020 ◽  
Vol 91 (5) ◽  
pp. 2851-2861
Author(s):  
Yuchen Wang ◽  
Kenji Satake ◽  
Takuto Maeda ◽  
Masanao Shinohara ◽  
Shin’ichi Sakai
Keyword(s):  
Real Time ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Detection Methods ◽  
Ocean Bottom ◽  
Intrinsic Mode Functions ◽  
The 2011 Tohoku Earthquake ◽  
Tsunami Early Warning ◽  
Tsunami Detection ◽  
Mode Decomposition

Abstract We propose a method of real-time tsunami detection using ensemble empirical mode decomposition (EEMD). EEMD decomposes the time series into a set of intrinsic mode functions adaptively. The tsunami signals of ocean-bottom pressure gauges (OBPGs) are automatically separated from the tidal signals, seismic signals, as well as background noise. Unlike the traditional tsunami detection methods, our algorithm does not need to make a prediction of tides. The application to the actual data of cabled OBPGs off the Tokohu coast shows that it successfully detects the tsunami from the 2016 Fukushima earthquake (M 7.4). The method was also applied to the extremely large tsunami from the 2011 Tohoku earthquake (M 9.0) and extremely small tsunami from the 1998 Sanriku earthquake (M 6.4). The algorithm detected the former huge tsunami that caused devastating damage, whereas it did not detect the latter microtsunami, which was not noticed on the coast. The algorithm was also tested for month-long OBPG data and caused no false alarm. Therefore, the algorithm is very useful for a tsunami early warning system, as it does not require any earthquake information to detect the tsunamis. It detects the tsunami with a short-time delay and characterizes the tsunami amplitudes accurately.

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