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In general, the main technique to eliminate noise from the signal of partial discharge can be realized in the time and frequency domain. The analysis of the partial discharge signal in frequency and time domain is the basic methodology in signal processing. When the signal is processed in the frequency domain, the time domain information would be lost. To resolve these weaknesses, the wavelet transform...
One of the major challenges in on-site Partial Discharge (PD) measurement is de-noising PD signals, which are normally being coupled with strong external noise. Therefore, Wavelet Transform (WT) techniques are being adopted in PD signal extraction. However due to their inherent shortcomings, online PD measurement may give wrong assessment. This paper proposes an Entropy-Based Wavelet Transform (EBWT)...
The detection of partial discharge (PD) signals has proven extremely important to diagnose the integrity of the insulation in high voltage equipments. The measurements of such signals are often accompanied by noise from different sources, which can compromise the data analysis. Numerous wavelet shrinkage denoising techniques have been discussed recently in the literature. This article proposes an...
This paper proposes an approach to determining classification of partial discharge (PD) events in Gas Insulated Load Break Switches (GILBS). Discrete wavelet transform (DWT) is employed to suppress noises of measured signals by the high-frequency current transformer (HFCT). Three kinds of different defects are designed and placed inside three GILBS individually. For accurately determination of the...
Partial Discharge (PD) measurement is widely adopted for assessing the insulation conditions of high voltage (HV) equipment. Wavelet transformation (WT) is one the de-noising techniques to extract PD signals from a variety of environmental noises and interferences. In wavelet-based PD signal de-noising, mother wavelet selection is one of the major challenges. This paper proposes a novel level-based...
Partial Discharge (PD) measurement and analysis is now a popular method in condition monitoring and dielectric diagnosis of power transformers. Discrete Wavelet Transform (DWT) is a powerful and well known method to analyze PD data in both time and frequency domains. In this contribution, the application of this transform on de-noising of recorded PD signals, defect classification and separating PD...
This paper introduces a new method to separate PD from other disturbing signals present on the high voltage generators and motors. The method is based on combination of a pattern classifier (i.e. discrete wavelet transform (DWT)) to de-noise PD and time-of-arrival method to separate PD sources. Furthermore we will show that it can recognize PD sources including rotating machine's internal and external...
Partial discharge detection in power transformers is discussed using a new approach that exploit the broad band of the Rogowski coils and the potential of two signal processing tools: discrete wavelet transform and empirical mode decomposition. Detecting and analyzing incipient activities of partial discharge can provide useful information to diagnostics and prognostics about transformer insulation...
Aiming at the harsh electromagnetic environment, this paper presents a new method to extract partial discharge signal, which using fast Fourier transform suppress discrete spectrum interference, and then use discrete wavelet transform delete the residual noise all together. Further more, there is also bring out how to modulate the fast Fourier transform denoising threshold to achieve the best denoising...
In terms of locating the source of partial discharge, transient earth voltage (TEV) detection is well recognised to have advantages over electrical methods. A variety of sensors are suitable for on-line PD testing of outdoor equipment, including ultra-high frequency (UHF) and radio frequency (RF) antenna, transient earth voltage (TEV) sensors, acoustic emission (AE) sensors and high frequency current...
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