A New Method to Detect Harmonics and Inter-Harmonics Based on Hilbert Marginal Spectrum

2012 ◽  
Vol 229-231 ◽  
pp. 1060-1063
Author(s):  
Mao Fa Gong ◽  
Guo Liang Li ◽  
Wen Hua Xia ◽  
Qing Xue Liu ◽  
Jing Jing Wang
Keyword(s):  
Power System ◽  
New Method ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
System A ◽  
Mode Decomposition ◽  
Harmonic Pollution ◽  
Marginal Spectrum ◽  
Selection Of

Aiming at the problem that harmonic pollution is becoming more and more serious in power system, a new method to detect harmonics and inter-harmonics based on Hilbert marginal spectrum is proposed in this paper. Firstly, the original signal is decomposed into several Intrinsic Mode Functions through Empirical Mode Decomposition. Then Hilbert marginal spectrum is obtained through Hilbert Huang Transform. It contains the information of signal’s harmonics frequency and those amplitudes. Finally, both harmonics and inter-harmonics are detected by this method. Fourier transform lacks the ability of time-frequency analysis. Wavelet transform is affected by the selection of wavelet base. This method overcomes these shortages and can detect the component of each harmonic quickly and accurately. Simulation result verifies that this method can meet the requirement of voltage and current distortion detection in power system.

A New Spectral Representation of Earthquake Recordings Using an Improved Hilbert-Huang Transform

2011 ◽  
Vol 255-260 ◽  
pp. 1671-1675
Author(s):  
Tian Li Huang ◽  
Wei Xin Ren ◽  
Meng Lin Lou
Keyword(s):  
Empirical Mode Decomposition ◽  
Spectral Representation ◽  
Low Frequency ◽  
Accurate Information ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Marginal Spectrum

A new spectral representation method of earthquake recordings using an improved Hilbert-Huang transform (HHT) is proposed in the paper. Firstly, the problem that the intrinsic mode functions (IMFs) decomposed by the empirical mode decomposition (EMD) in HHT is not exactly orthogonal is pointed out and improved through the Gram-Schmidt orthogonalization method which is referred as the orthogonal empirical mode decomposition (OEMD). Combined the OEMD and the Hilbert transform (HT) which is referred as the improved Hilbert-Huang transform (IHHT), the orthogonal intrinsic mode functions (OIMFs) and the orthogonal Hilbert spectrum (OHS) and the orthogonal Hilbert marginal spectrum (OHMS) are obtained. Then, the IHHT has been applied for the analysis of the El Centro earthquake recording. The obtained spectral representation result shows that the OHS gives more detailed and accurate information in a time–frequency–energy presentation than the Hilbert spectrum (HS) and the OHMS gives more faithful low-frequency energy presentation than the Fourier spectrum (FS) and the Hilbert marginal spectrum (HMS).

scholarly journals Texture Feature Extraction Method for Ground Nephogram Based on Hilbert Spectrum of Bidimensional Empirical Mode Decomposition

2014 ◽  
Vol 31 (9) ◽  
pp. 1982-1994 ◽  
Author(s):  
Xiaoying Chen ◽  
Aiguo Song ◽  
Jianqing Li ◽  
Yimin Zhu ◽  
Xuejin Sun ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Feature Extraction Method ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Bidimensional Empirical Mode Decomposition

Abstract It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.

The Application of Improved HHT to Harmonic Analysis and Fault Detection of Power System

2011 ◽  
Vol 354-355 ◽  
pp. 1406-1411
Author(s):  
Wen Hua Han ◽  
Hai Xia Ren ◽  
Xu Chen ◽  
Xiao Juan Tao
Keyword(s):  
Fault Detection ◽  
Power System ◽  
Harmonic Analysis ◽  
Harmonic Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Frequency Domain Analysis ◽  
Analysis Method ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition

Hilbert-Huang transform (HHT) is a new time-frequency-domain analysis method, which is suitable for non-stationary and nonlinear signals. In this paper, endpoint continuation and ensemble empirical mode decomposition (EEMD) decomposition method are introduced to improve the HHT, which solve the endpoint winger and modal aliasing problem. The improved HHT (IHHT) is used for analyzing the harmonic signal and detecting the fault signal of power system. Simulation results show that IHHT is feasible and effective for harmonic analysis and fault detection.

scholarly journals ANALYSIS OF TEMPORAL VARIATIONS IN TURBIDITY FOR A COASTAL AREA USING THE HILBERT-HUANG-TRANSFORM

2011 ◽  
Vol 1 (32) ◽  
pp. 25
Author(s):  
Shigeru Kato ◽  
Magnus Larson ◽  
Takumi Okabe ◽  
Shin-ichi Aoki
Keyword(s):  
Instantaneous Frequency ◽  
River Mouth ◽  
Original Data ◽  
Temporal Variations ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
The Hilbert Transform

Turbidity data obtained by field observations off the Tenryu River mouth were analyzed using the Hilbert-Huang Transform (HHT) in order to investigate the characteristic variations in time and in the frequency domain. The Empirical Mode Decomposition (EMD) decomposed the original data into only eight intrinsic mode functions (IMFs) and a residue in the first step of the HHT. In the second step, the Hilbert transform was applied to the IMFs to calculate the Hilbert spectrum, which is the time-frequency distribution of the instantaneous frequency and energy. The changes in instantaneous frequencies showed correspondence to high turbidity events in the Hilbert spectrum. The investigation of instantaneous frequency variations can be used to understand transitions in the state of the turbidity. The comparison between the Fourier spectrum and the Hilbert spectrum integrated in time showed that the Hilbert spectrum makes it possible to detect and quantify the cycle of locally repeated events.

scholarly journals Data Interpretation Technology of GPR Survey Based on Variational Mode Decomposition

2019 ◽  
Vol 9 (10) ◽  
pp. 2017 ◽  
Author(s):  
Juncai Xu ◽  
Bangjun Lei
Keyword(s):  
Instantaneous Frequency ◽  
Data Interpretation ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Synthetic Signal ◽  
Mode Decomposition ◽  
The Media ◽  
Ground Penetrating

Data interpretation is the crucial scientific component that influences the inspection accuracy of ground penetrating radar (GPR). Developing algorithms for interpreting GPR data is a research focus of increasing interest. The problem of algorithms for interpreting GPR data is unresolved. To this end, this study proposes a sophisticated algorithm for interpreting GPR data with the aim of improving the inspection resolution. The algorithm is formulated by integrating variational mode decomposition (VMD) and Hilbert–Huang transform techniques. With this method, the intrinsic mode function of the GPR data is first produced using the VMD of the data, followed by obtaining the instantaneous frequency by using the Hilbert–Huang transform to analyze the intrinsic mode functions. The instantaneous frequency data can be decomposed into three frequency attributes, including frequency division section, time-frequency section, and space frequency section, which constitute a platform to gain insight into the nature of the GPR data, such that the inspected media components can be examined. The effectiveness of the proposed method on a synthetic signal from a GPR forward model was studied, with the multi-resolution performance being tested. Inspecting the media of a highroad by analyzing the GPR data, with the abnormal characteristics being designated, validated the applicability of the proposed method.

Time-Frequency Analysis for Power System Oscillations

2013 ◽  
Vol 336-338 ◽  
pp. 928-931
Author(s):  
Chia Liang Lu ◽  
Pei Hwa Huang
Keyword(s):  
Power Systems ◽  
Power System ◽  
Frequency Analysis ◽  
Low Frequency ◽  
Normal Operation ◽  
Intrinsic Mode Functions ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Low Frequency Oscillations

Low frequency oscillations due to the lack of damping may occur in power systems under normal operation and will cause system instability. These oscillations are essentially nonlinear power responses which are difficult to extract the inherent characteristics by the time domain method. This paper aims to analyze nonlinear power responses by using the Hilbert-Huang transform (HHT) which is a time-frequency signal processing method which comprises steps of the empirical mode decomposition and the Hilbert transform. Dynamic power system responses, including generator output power and line power are to be processed by the HHT and a set of intrinsic mode functions and the associated Hilbert spectrum are obtained. The generator with most effects on the system will be accordingly found out through the time-frequency analysis and the power system stabilizer will be placed at the generator. Numerical results from a sample power system are demonstrated to show the validity of the time-frequency approach in the study of power system low frequency oscillations.

Empirical Mode Decomposition of AE Signal Processing Based on Hilbert-Huang Transformation

2009 ◽  
Author(s):  
Wei Li ◽  
Guang Dai ◽  
Ying Zhang ◽  
Feifei Long ◽  
Yanru Wang
Keyword(s):  
Signal Processing ◽  
Local Characteristic ◽  
Characteristic Time Scale ◽  
Localization Property ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Filter Property ◽  
Ae Signals

In this paper a new signal processing method of Hilbert-Huang transform is applied to analyze AE lead-breaking signals and loaded concrete AE signals. Empirical model of decomposition (EMD) and cubic interpolation were used in AE signals analysis which was based on the local characteristic time scale. After the decomposition inherent model function (IMF) can effectively preserve non-linear and non-stationary features of signals during the processing. Meanwhile, noises of signals were eliminated by using multi-dimension filter property of EMD. The above two signal’s IMF can be clearly shown on a time-frequency vs. energy distribution spectrum and marginal spectrum by Hilbert-Huang transformation. The results show that HHT can efficiently reflecting the intrinsic properties of signals. Besides, HHT method, which is more adaptive than other methods, had a bigger superiority in adaptability and frequency concentration, and it has batter localization property and visual result of the time-frequency domain.

scholarly journals A Reservoir Information Extraction Method Based on Improved Hilbert-Huang Transform

2019 ◽  
pp. 85-90
Author(s):  
Qingmi Yang
Keyword(s):  
Signal Processing ◽  
Seismic Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Intrinsic Mode Functions ◽  
Signal Processing Technique ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Non Stationary Signal

Hilbert-Huang transform (HHT) is a nonlinear non-stationary signal processing technique, which is more effective than traditional time-frequency analysis methods in complex seismic signal processing. However, this method has problems such as modal aliasing and end effect. The problem causes the accuracy of signal processing to drop. Therefore, this paper introduces the method of combining the Ensemble Empirical Mode Decomposition (EEMD) and the Normalized Hilbert transform (NHT) to extract the instantaneous properties. The specific process is as follows: First, the EEMD method is used to decompose the seismic signal to a series of Intrinsic Mode Functions (IMF), and then The IMFs is screened by using the relevant properties, and finally the NHT is performed on the IMF to obtain the instantaneous properties.

FAST EMPIRICAL MODE DECOMPOSITIONS OF MULTIVARIATE DATA BASED ON ADAPTIVE SPLINE-WAVELETS AND A GENERALIZATION OF THE HILBERT–HUANG–TRANSFORMATION (HHT) TO ARBITRARY SPACE DIMENSIONS

2010 ◽  
Vol 02 (03) ◽  
pp. 337-358 ◽  
Author(s):  
ROLAND PABEL ◽  
ROBIN KOCH ◽  
GABRIELA JAGER ◽  
ANGELA KUNOTH
Keyword(s):  
Hilbert Transform ◽  
Multivariate Data ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Local Means ◽  
Spline Wavelets ◽  
Mode Decomposition ◽  
Arbitrary Space ◽  
The Hilbert Transform

The Hilbert–Huang-Transform (HHT) has proven to be an appropriate multiscale analysis technique specifically for nonlinear and nonstationary time series on non-equidistant grids. It is empirically adapted to the data: first, an additive decomposition of the data (empirical mode decomposition, EMD) into certain multiscale components is computed, denoted as intrinsic mode functions. Second, to each of these components, the Hilbert transform is applied. The resulting Hilbert spectrum of the modes provides a localized time-frequency spectrum and instantaneous (time-dependent) frequencies. For the first step, the empirical decomposition of the data, a different method based on local means has been developed by Chen et al. (2006). In this paper, we extend their method to multivariate data sets in arbitrary space dimensions. We place special emphasis on deriving a method which is numerically fast also in higher dimensions. Our method works in a coarse-to-fine fashion and is based on adaptive (tensor-product) spline-wavelets. We provide some numerical comparisons to a method based on linear finite elements and one based on thin-plate-splines to demonstrate the performance of our method, both with respect to the quality of the approximation as well as the numerical efficiency. Second, for a generalization of the Hilbert transform to the multivariate case, we consider the Riesz transformation and an embedding into Clifford-algebra valued functions, from which instantaneous amplitudes, phases and orientations can be derived. We conclude with some numerical examples.

Vibration Signal Analysis and Fault Diagnosis of Gears Based on Hilbert Marginal Spectrum

2014 ◽  
Vol 909 ◽  
pp. 121-126 ◽  
Author(s):  
Jiang Ping Wang ◽  
Jin Cui
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Operating Conditions ◽  
Characteristic Time Scale ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Vibration Signal Analysis ◽  
Non Stationary Signal

Hilbert-Huang transform is a new method of signal processing, which is very suitable for dealing with nonlinear and non-stationary signal. In this article, a gear fault diagnosis method based on Hilbert marginal spectrum is proposed in view of the non-stationary characteristics of gear vibration signal. First the original vibration signal is decomposed into several intrinsic mode functions (IMF) of different characteristic time scale smoothly by means of empirical mode decomposition (EMD) method. Then the Hilbert-Huang transform is carried out for IMF and the Hilbert marginal spectrum under different operating conditions are obtained. Gear faults can be judged through the analysis of the marginal spectrum. The experimental results show that this method can effectively diagnose the gear faults.

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