An Improved Methodological Approach for Denoising of Partial Discharge Data by the Wavelet Transform

Abstract

Abstract-Partial Discharge (PD) measurements may be affected by external noise and disturbances of various natures such as interference from broadcasting stations, stochastic noise, pulses from power electronics, etc. Extracting PD pulses from such a noisy environment is therefore a crucial issue. This paper presents a wavelet based technique for automatic noise rejection. The core of the paper is the use of an improved methodological approach for the selection of a suitable wavelet, which aims at summing up the benefits and overcoming some limitations of previous techniques. Firstly, a very wide set of training signals is used for the identification of the decomposition level and for the calculation of suitable performance parameters that identify each wavelet; then a Performance Fingerprint is introduced in order to summarize the ability of a specific wavelet to reconstruct a partial discharge waveform, and a distance criterion is used for the selection of the most suitable wavelet. Afterwards, useful information is collected for the reconstruction of the PD signal, and finally, results on the application of the algorithm for a set of numerical and experimental signals are presented.

Creeping discharges features propagating in air at atmospheric pressure on various materials under positive lightning impulse voltage – part 1: noise suppression using the discrete wavelet transform approach

“…4 Digital signal processing using wavelets 4.1 1D discrete wavelet transform 4.1.1 Theoretical background: Due to the rapid development of the wavelet theory, PDs de-noising of 1D signals is increasingly adopted according to decomposition and reconstruction principles [42][43][44]. Wavelets are a set of functions that represent transient phenomena and result from a dilation and shift of the original waveform [45].…”

Enhanced Partial Discharge Signal Denoising Using Dispersion Entropy Optimized Variational Mode Decomposition

“…The matching impedance, is either RC circuit for wide-band PD detection or RLC circuit for narrow-band detection as shown in Figure 2 . The detector outputs different pulse shapes based on the type of detection circuit, which is realized as the natural response of either parallel RC or RLC circuit [ 7 , 40 ]. These are Damped Exponential Pulse (DEP) and Damped Oscillatory Pulse (DOP).…”

Improving RF-Based Partial Discharge Localization via Machine Learning Ensemble Method

“…Kaiser's rule [25] is used to automatically select the minimum numbers of retained principal components (components associated with eigenvalues exceeding the mean of all eigenvalues). The Daubechies wavelet db14 with a 5th level decomposition which has been used for PD de-noising [26] is adopted in this work. Owing to the fact that only a few of the wavelet coefficients describing PD waveform carry significant information, hardthresholding is employed.…”