Partial Discharge Detection of On-Line Cables Based on Hilbert Huang Transform

2014 ◽  
Vol 960-961 ◽  
pp. 759-762
Author(s):  
Fang Li
Keyword(s):  
Spline Interpolation ◽  
Partial Discharge ◽  
Test System ◽  
Practical Significance ◽  
Grid Security ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
On Line ◽  
Overshoot And Undershoot ◽  
Lock In

Hilbert-Huang transforms calculation method was used in the study on signal of partial discharge from on-line cable. The partial discharge signal was made to be intrinsic mode function (IMF) by empirical mode decomposition (EMD).The cubic spline interpolation was selected as the interpolation function and ideal envelope curve was achieved to eliminates defects such as overshoot and undershoot effectively. Appropriate lock-in amplifier was choosed and pre-amplifier and phase sensitive detection circuit was designed to determine the calculation programs. The results showed that the test system can effectively capture the weak PD signals in cable under complex environmental conditions and the PD signal waveform obtained was improved through Hilbert-Huang transforms.The state of insulation and the remaining life of the cable can be judged effectively,which has practical significance for improving the grid security.

scholarly journals Time-frequency analysis of DC bias vibration of transformer core on the basis of Hilbert–Huang transform

2016 ◽  
Vol 36 (1) ◽  
pp. 90-97 ◽  
Author(s):  
Xingmou Liu ◽  
Yongming Yang ◽  
Fan Yang ◽  
Qingyan Shi
Keyword(s):  
Frequency Analysis ◽  
Test System ◽  
Vibration Measurement ◽  
Intrinsic Mode Functions ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Vibration Strength ◽  
Mode Decomposition ◽  
Dc Bias

<p>This paper presents a time–frequency analysis of the vibration of transformer under direct current (DC) bias through Hilbert–Huang transform (HHT). First, the theory of DC bias for the transformer was analyzed. Next, the empirical mode decomposition (EMD) process, which is the key in HHT, was introduced. The results of EMD, namely, intrinsic mode functions (IMFs), were calculated and summed by Hilbert transform(HT) to obtain time-dependent series in a 2D time–frequency domain. Lastly, a test system of vibration measurement for the transformer was set up. Three direction (x, y, and z axes) components of core vibration were measured. Decomposition of EMD and HHT spectra showed that vibration strength increased, and odd harmonics were produced with DC bias. Results indicated that HHT is a viable signal processing tool for transformer health monitoring.</p>

Research on Ultrasonic Testing of Coarse-Grain Materials with Hilbert-Huang Transform

2013 ◽  
Vol 820 ◽  
pp. 97-101 ◽  
Author(s):  
Qiu Feng Li ◽  
Yu Wang ◽  
Lu Ying Xi
Keyword(s):  
Ultrasonic Testing ◽  
Signal To Noise Ratio ◽  
Test System ◽  
Experimental Result ◽  
Coarse Grain ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Testing Signal

In ultrasonic testing of coarse-grain materials, signal to noise ratio (SNR) is so poor because of the serious structure noise, and reflected wave from defects is difficult to be identified. In order to improve SNR and increase the reliability of ultrasonic testing for coarse grain materials, Hilbert-Huang Transform (HHT) is introduced to analyze and process the testing signal here. Firstly, detected signals from the coarse grain material can be collected by using ultrasonic test system; And then many Intrinsic Mode Function (IMF) can be obtained according to Empirical Mode Decomposition (EMD), and marginal spectrum of different mode can be gotten by Hilbert transform; And finally, the noise should be removed after analyzing the time-frequency information, and SNR is able to be enhanced and the reflection wave from defect is being more obvious. It was shown from the experimental result that the ineffective structure noise could be removed after HHT, and SNR could be improved and the defect reflection is more outstanding.

A novel GNSS deformation feature extraction method based on ensemble improved LMD threshold denoising

2020 ◽  
Vol 14 (4) ◽  
pp. 445-453
Author(s):  
Qian Fan ◽  
Yiqun Zhu
Keyword(s):  
Feature Extraction ◽  
Spline Interpolation ◽  
Simulated Data ◽  
Mining Area ◽  
Deformation Monitoring ◽  
Denoising Method ◽  
Feature Extraction Method ◽  
Deformation Feature ◽  
Mode Decomposition ◽  
Better Than

AbstractIn order to solve the problem that the moving span of basic local mean decomposition (LMD) method is difficult to choose reasonably, an improved LMD method (ILMD), which uses three cubic spline interpolation to replace the sliding average, is proposed. On this basis, with the help of noise aided calculation, an ensemble improved LMD method (EILMD) is proposed to effectively solve the modal aliasing problem in original LMD. On the basis of using EILMD to effectively decompose the data of GNSS deformation monitoring series, GNSS deformation feature extraction model based on EILMD threshold denoising is given by means of wavelet soft threshold processing mode and threshold setting method in empirical mode decomposition denoising. Through the analysis of simulated data and the actual GNSS monitoring data in the mining area, the results show that denoising effect of the proposed method is better than EILMD, ILMD and LMD direct coercive denoising methods. It is also better than wavelet analysis denoising method, and has good adaptability. This fully demonstrates the feasibility and effectiveness of the proposed method in GNSS feature extraction.

scholarly journals Novel Features and PRPD Image Denoising Method for Improved Single-Source Partial Discharges Classification in On-Line Hydro-Generators

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3267
Author(s):  
Ramon C. F. Araújo ◽  
Rodrigo M. S. de Oliveira ◽  
Fernando S. Brasil ◽  
Fabrício J. B. Barros
Keyword(s):  
Image Denoising ◽  
Recognition Performance ◽  
Partial Discharge ◽  
Classification Performance ◽  
Partial Discharges ◽  
Denoising Method ◽  
Internal Parameters ◽  
On Line ◽  
Automatic Removal ◽  
The Impact

In this paper, a novel image denoising algorithm and novel input features are proposed. The algorithm is applied to phase-resolved partial discharge (PRPD) diagrams with a single dominant partial discharge (PD) source, preparing them for automatic artificial-intelligence-based classification. It was designed to mitigate several sources of distortions often observed in PRPDs obtained from fully operational hydroelectric generators. The capabilities of the denoising algorithm are the automatic removal of sparse noise and the suppression of non-dominant discharges, including those due to crosstalk. The input features are functions of PD distributions along amplitude and phase, which are calculated in a novel way to mitigate random effects inherent to PD measurements. The impact of the proposed contributions was statistically evaluated and compared to classification performance obtained using formerly published approaches. Higher recognition rates and reduced variances were obtained using the proposed methods, statistically outperforming autonomous classification techniques seen in earlier works. The values of the algorithm’s internal parameters are also validated by comparing the recognition performance obtained with different parameter combinations. All typical PD sources described in hydro-generators PD standards are considered and can be automatically detected.

Two-dimensional empirical mode decomposition by finite elements

2006 ◽  
Vol 462 (2074) ◽  
pp. 3081-3096 ◽  
Author(s):  
Y Xu ◽  
B Liu ◽  
J Liu ◽  
S Riemenschneider
Keyword(s):  
Finite Element ◽  
Linear Combination ◽  
Empirical Mode Decomposition ◽  
Spline Interpolation ◽  
Basis Functions ◽  
Two Dimensional ◽  
Element Analysis ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Mode Decomposition

Empirical mode decomposition (EMD) is a powerful tool for analysis of non-stationary and nonlinear signals, and has drawn significant attention in various engineering application areas. This paper presents a finite element-based EMD method for two-dimensional data analysis. Specifically, we represent the local mean surface of the data, a key step in EMD, as a linear combination of a set of two-dimensional linear basis functions smoothed with bi-cubic spline interpolation. The coefficients of the basis functions in the linear combination are obtained from the local extrema of the data using a generalized low-pass filter. By taking advantage of the principle of finite-element analysis, we develop a fast algorithm for implementation of the EMD. The proposed method provides an effective approach to overcome several challenging difficulties in extending the original one-dimensional EMD to the two-dimensional EMD. Numerical experiments using both simulated and practical texture images show that the proposed method works well.

Sign in / Sign up

Export Citation Format

Share Document