A novel disturbance identification method based on empirical mode decomposition for HVDC transmission line protection

2014 ◽  
Author(s):  
Fei Kong ◽  
Baohui Zhang
Keyword(s):  
Transmission Line ◽  
Empirical Mode Decomposition ◽  
Hvdc Transmission ◽  
Identification Method ◽  
Mode Decomposition ◽  
Transmission Line Protection

Transmission Line Fault Location Based on Hilbert-Huang Transformation and SVM

2013 ◽  
Vol 333-335 ◽  
pp. 1673-1678
Author(s):  
Ke Qin Bao ◽  
Bao Xing Wu ◽  
Yun Hui Xu
Keyword(s):  
Support Vector Machine ◽  
Transmission Line ◽  
Empirical Mode Decomposition ◽  
Fault Location ◽  
Signal Sequence ◽  
Hilbert Transformation ◽  
Support Vector ◽  
End Effect ◽  
Positioning Accuracy ◽  
Mode Decomposition

In the process of the Hilbert-Huang Transformation, empirical mode decomposition (EMD) and Hilbert Transformation of the IMF components may result in the terminal effect, utilizing the support vector machine (SVM) extend the signal sequence and IMF components to weaken the end effect. The paper analyzes the fault signal which extracted under the different fault conditions to complete the fault location. The simulation result shows that using SVM can effectively restrain terminal effect; In the different fault states can have a high positioning accuracy.

The Applied Research of the Hilbert-Huang Transform and Wavelet Transform in the Fault Location of Transmission Line

2013 ◽  
Vol 291-294 ◽  
pp. 2432-2436
Author(s):  
Zhi Bin Li ◽  
Bao Xing Wu ◽  
Yun Hui Xu
Keyword(s):  
Wavelet Transform ◽  
Transmission Line ◽  
Empirical Mode Decomposition ◽  
Fault Location ◽  
Ensemble Empirical Mode Decomposition ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Decomposition Processes ◽  
Before And After ◽  
Better Than

In the process of the Hilbert-Huang transform, empirical mode decomposition (EMD) may result in the end effect and modal aliasing when processing data, so proposing Ensemble Empirical Mode Decomposition (EEMD) instead of EMD, and assessing the accuracy of the two decomposition processes according to the total energy of the signal before and after the decomposition. Take a comparison between the Hilbert-Huang transform and the wavelet transform, the localization showed that the Hilbert-Huang transform is better than wavelet transform in the fault location of transmission line.

Identification of MDOF Non-Linear Uncoupled Dynamical Systems Using Hilbert Transform and Empirical Mode Decomposition Method

2011 ◽  
Vol 255-260 ◽  
pp. 1676-1680
Author(s):  
Tian Li Huang ◽  
Wei Xin Ren ◽  
Meng Lin Lou
Keyword(s):  
Dynamical Systems ◽  
Empirical Mode Decomposition ◽  
Hilbert Transform ◽  
Degree Of Freedom ◽  
Intrinsic Mode Functions ◽  
Nonlinear Properties ◽  
Identification Method ◽  
Mode Decomposition ◽  
Non Linear ◽  
The Hilbert Transform

A non-linear dynamical system identification method using Hilbert transform (HT) and empirical mode decomposition (EMD) is proposed. For a single-degree-of-freedom (SDOF) nonlinear system, the Hilbert transform identification method is good at identifying the instantaneous modal parameters (natural frequencies, damping characteristics and their dependencies on a vibration amplitude and frequency). For the multi-degree-of-freedom (MDOF) non-linear uncoupled dynamical systems, the EMD method is attempting for the decomposition of response signals into a collection of mono-components signals, termed intrinsic mode functions (IMFs). Considering the IMFs admit a well-behaved Hilbert transform, the HT identification method has been applied for the identification of nonlinear properties. The numerical simulation of a 2-dof shear-beam building model with nonlinear stiffness illustrated the proposed technique.

A correlation based bullet identification method using empirical mode decomposition

2017 ◽  
Vol 278 ◽  
pp. 351-360 ◽  
Author(s):  
Saeed Bigdeli ◽  
Hamed Danandeh ◽  
Mohsen Ebrahimi Moghaddam
Keyword(s):  
Empirical Mode Decomposition ◽  
Identification Method ◽  
Mode Decomposition
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