scholarly journals Disturbance Elimination for Partial Discharge Detection in Spacer of Gas-Insulated Switchgears

2017 ◽  
Author(s):  
Guoming Wang ◽  
Gyung-Suk Kil ◽  
Hong-Keun Ji ◽  
Jong-Hyuk Lee
Keyword(s):  
Wavelet Transform ◽  
Partial Discharge ◽  
Detection Sensitivity ◽  
Discrete Wavelet ◽  
Mother Wavelet ◽  
Signal Energy ◽  
Monitoring And Diagnosis ◽  
Capacitive Component ◽  
Increasing Demand ◽  
Thresholding Function

With the increasing demand for precise condition monitoring and diagnosis of gas-insulated switchgears (GIS), it has become a challenge to improve the detection sensitivity of partial discharge (PD) induced in the GIS spacer. This paper deals with the elimination of the capacitive component from the phase resolved partial discharge (PRPD) signal generated in GIS spacer based on the discrete wavelet transform. Three types of typical insulation defects were simulated using PD cells. The single PD pulses were detected and were further used to determine the optimal mother wavelet. As a result, the bior6.8 was selected to decompose the PD signal into 8 levels and the signal energy at each level was calculated. The decomposed components related with capacitive disturbance were discarded whereas those associated with PD were de-noised by a threshold and a thresholding function. Finally, the PRPD signals were reconstructed using the de-noised components.

scholarly journals MODELLING OF PARTIAL DISCHARGE ANALYSIS SYSTEM USING WAVELET TRANSFORM DENOISING TECHNIQUE IN LABVIEW ENVIRONMENT

2019 ◽  
Vol 81 (6) ◽  
Author(s):  
A. Nazifah Abdullah ◽  
S. H. K. Hamadi ◽  
M. Isa ◽  
B. Ismail ◽  
A. N. Nanyan ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Virtual Instrument ◽  
Signal To Noise Ratio ◽  
Partial Discharge ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Mother Wavelet ◽  
Signal To Noise ◽  
Labview Software ◽  
Analysis System

Partial discharge (PD) measurement is an essential to detect and diagnose the existence of the PD. However, this measurement has faced noise disturbance in industrial environments. Thus, PD analysis system using discrete wavelet transform (DWT) denoising technique via Laboratory Virtual Instrument Engineering Workbench (LabVIEW) software is proposed to distinguish noise from the measured PD signal. In this work, the performance of denoising process is analyzed based on calculated mean square error (MSE) and signal to noise ratio (SNR). The result is manipulated based on Haar, Daubechies, Coiflets, Symlets and Biorthogonal type of mother wavelet with different decomposition levels. From the SNR results, all types of the mother wavelet are suitable to be used in denoising technique since the value of SNR is in large positive value. Therefore, further studies were conducted and found out that db14, coif3, sym5 and bior5.5 wavelets with least MSE value are considered good to be used in the denoising technique. However, bior5.5 wavelet is proposed as the most optimum mother wavelet due to consistency of producing minimum value of MSE and followed by db14.

Mother wavelet selection in the discrete wavelet transform for condition monitoring of wind turbine drivetrain bearings

Wind Energy ◽  
2019 ◽  
Vol 22 (11) ◽  
pp. 1581-1592 ◽  
Author(s):  
Daniel Strömbergsson ◽  
Pär Marklund ◽  
Kim Berglund ◽  
Juhamatti Saari ◽  
Allan Thomson
Keyword(s):  
Wavelet Transform ◽  
Wind Turbine ◽  
Discrete Wavelet Transform ◽  
Condition Monitoring ◽  
Discrete Wavelet ◽  
Mother Wavelet ◽  
Wavelet Selection

A method to capture and de-noise partial discharge pulses using discrete wavelet transform and ANFIS

2014 ◽  
Vol 25 (11) ◽  
pp. 2696-2712 ◽  
Author(s):  
Hamid Jahangir ◽  
Ehsan Hajipour ◽  
Mehdi Vakilian ◽  
Asghar Akbari ◽  
Trevor Blackburn ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Partial Discharge ◽  
Discrete Wavelet

ARTIFACT REMOVAL AND BRAIN RHYTHM DECOMPOSITION FOR EEG SIGNAL USING WAVELET APPROACH

2016 ◽  
Vol 78 (7-5) ◽  
Author(s):  
Syarifah Noor Syakiylla Sayed Daud ◽  
Rubita Sudirman
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Mother Wavelet ◽  
Decomposition Level ◽  
Biomedical Signal ◽  
Brain Rhythm ◽  
Time And Energy ◽  
Eeg Signal Processing

This recent study introduces and discusses briefly the use of wavelet approach in removing the artifacts and extraction of features for electroencephalography (EEG) signal. Many of new approaches have been discovered by the researcher for processing the EEG signal. Generally, the EEG signal processing can be divided into pre-processing and post-processing.  The aim of processing is to remove the unwanted signal and to extract important features from the signal.  However, the selections of non-suitable approach affect the actual result and wasting the time and energy.  Wavelet is among the effective approach that can be used for processing the biomedical signal.  The wavelet approach can be performed in MATLAB toolbox or by coding, that require a simple and basic command. In this paper, the application of wavelet approach for EEG signal processing is introduced. Moreover, this paper also discusses the effect of using db3 mother wavelet with 5th decomposition level of stationary wavelet transform and db4 mother wavelet with 7th decomposition level of discrete wavelet transform in removing the noise and decomposing of the brain rhythm. Besides, the simulation result are also provided for better configuration.

scholarly journals Denoising of Heavily Contaminated Partial Discharge signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6540
Author(s):  
Mohammed A. Shams ◽  
Hussein I. Anis ◽  
Mohammed El-Shahat
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
High Voltage ◽  
Signal To Noise Ratio ◽  
Partial Discharge ◽  
Gaussian White Noise ◽  
Discrete Wavelet ◽  
Power Cable ◽  
Power Cables ◽  
Algorithm Performance

Online detection of partial discharges (PD) is imperative for condition monitoring of high voltage equipment as well as power cables. However, heavily contaminated sites often burden the signals with various types of noise that can be challenging to remove (denoise). This paper proposes an algorithm based on the maximal overlap discrete wavelet transform (MODWT) to denoise PD signals originating from defects in power cables contaminated with various levels of noises. The three most common noise types, namely, Gaussian white noise (GWN), discrete spectral interference (DSI), and stochastic pulse shaped interference (SPI) are considered. The algorithm is applied to an experimentally acquired void-produced partial discharge in a power cable. The MODWT-based algorithm achieved a good improvement in the signal-to-noise ratio (SNR) and in the normalized correlation coefficient (NCC) for the three types of noises. The MODWT-based algorithm performance was also compared to that of the empirical Bayesian wavelet transform (EBWT) algorithm, in which the former showed superior results in denoising SPI and DSI, as well as comparable results in denoising GWN. Finally, the algorithm performance was tested on a PD signal contaminated with the three type of noises simultaneously in which the results were also superior.

Partial Discharge Location through Application of Stationary Discrete Wavelet Transform on UHF Signals

2021 ◽  
pp. 1-1
Author(s):  
George V. R. Xavier ◽  
Rodrigo A. Coelho ◽  
Hugerles S. Silva ◽  
Alexandre J. R. Serres ◽  
Edson G. Da Costa ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Partial Discharge ◽  
Discrete Wavelet ◽  
Discharge Location
Sign in / Sign up

Export Citation Format

Share Document