Achieving acceptable levels of sensitivity during online and/or onsite partial discharge (PD) measurements still continues to remain a very challenging task, primarily due to strong coupling of external (random, discrete spectral and stochastic pulsive) interferences. Many analog and digital approaches have been proposed for suppressing these interferences, and amongst these, rejection of the pulsive type of interferences is known to be very difficult, if not impossible. The time and frequency characteristics of the pulsive interference being very similar to that of the PD pulses is the main reason posing difficulty in their separation. In this paper, a novel, semi-automatic, and empirical wavelet-based method (using multi-resolution signal analysis) is proposed to recover PD pulses, buried in excessive noise/interference comprising of random, discrete spectral, pulsive, and any combination of these interferences occurring simultaneously and overlapping-in-time with the PD pulses. A critical assessment of the proposed method is carried out, by processing both simulated and practically acquired PD signals. The results obtained are also compared with those from the best digital filter (infinite impulse response, IIR and finite impulse response, FIR) method proposed in literature. From the results it emerges that, the wavelet approach is superior and further, has the unique capability of successfully rejecting all the three kinds of interferences, even when PD signals and one or all interferences occur simultaneously and overlap-in-time.

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Wavelet-based denoising of partial discharge signals buried in excessive noise and interference

Medium and high voltage power cables are widely used in the electrical industry with substantial growth over the last 20-30 years ago, particular in the use of XLPE insulated systems. Ageing of the cable insulation is becoming an increasing problem that requires development of reliable methods for on-line condition assessment. For insulation condition assessment of MV and HV cables, partial discharge (PD) monitoring is one of the most effective techniques. However on-site and on-line PD measurements are affected by electromagnetic interference (EMI) that makes sensitive PD detection very difficult, if not impossible. This paper describes implementation of wavelet transform techniques to reject noise from on-line partial discharge measurements on cables. A new wavelet threshold determination method is proposed with the technique. With implementation of this novel de-noising method, PD measurement sensitivity has been greatly improved. In addition, a full AC cycle data recovery can be achieved instead of focusing only on recovering individual PD pulses. Other wavelet threshold de-noising methods are discussed and examined under a noisy environment to compare their performance with the new method proposed here. The method described here has been found to be superior to the other wavelet-based methods

A novel wavelet transform technique for on-line partial discharge measurements. 1. WT de-noising algorithm

Noise rejection, defect identification and degradation diagnosis in on-field partial discharge measurements are sought by industry, but hardly achieved in practice. This paper presents tools for automatic noise suppression in measurements performed by ultra wide band digitizers, able to record a large quantity of partial discharge (PD) pulse waveforms. Noise and PD signals are split in different classes on the basis of their shape by means of a fuzzy classifier. Tools used for establishing whether a given class of recorded signals is due to external noise or not are proposed. As an example, two kinds of noise are considered: random noise and rectifier-generated noise. A companion paper will explain how the same classification tools can be employed for the purpose of defect identification.

Advanced PD inference in on-field measurements. I. Noise rejection

One of the major challenges of on-site partial discharge (PD) measurements is the recovery of PD signals from a noisy environment. The different sources of noise include thermal or resistor noise added by the measuring circuit, and high-frequency sinusoidal signals that electromagnetically couple from radio broadcasts and/or carrier wave communications. Sophisticated methods are required to detect PD signals correctly. Fortunately, advances in analog-to-digital conversion (ADC) technology, and recent developments in digital signal processing (DSP) enable easy extraction of PD signals. This paper deals with the denoising of PD signals caused by corona discharges. Several techniques are investigated and employed on simulated as well as real PD data.

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Signal denoising techniques for partial discharge measurements

Wavelet shrinkage methods are effective for de-noising of partial discharge (PD) detection. Base wavelets are related to distortion of PD signals de-noised by wavelet shrinkage methods. This paper presents a scale dependent wavelet selection scheme for de-noising of PD detection. The scale dependent wavelet selection scheme is called the energy based wavelet selection (EBWS) because an energy criterion is proposed for the scheme. In the proposed energy criterion, a base wavelet is selected as an optimal base wavelet if it can generate an approximation with the largest energy among all base wavelets for selection at each scale. PD high-frequency signals are simulated and PD ultra-high-frequency signals are obtained by experiments in laboratory for de-noising experiments and analysis. In comparison with the correlation-based wavelet selection (CBWS) scheme, the wavelet shrinkage, based on the EBWS, generates significantly smaller waveform distortion and magnitude errors of de-noised PD signals.

Scale dependent wavelet selection for de-noising of partial discharge detection

A critical study of the performance of several digital filters for rejecting discrete spectral interference (DSI) in partial-discharge (PD) testing is reported. The filters are evaluated with particular reference to distortion introduced on successive PD pulses. A digital filtering method based on a cascaded second-order IIR lattice notch filter is proposed. The study shows that, for similar performance, methods based on linear prediction require longer filter orders when compared to cascaded IIR notch filters. The nonadaptive methods are found to be stable against impulsive disturbances, while adaptive methods are unstable. The method proposed gives the best performance, low distortion to a sequence of PD pulses, good stability, and fast filtering time. The performance at higher sampling rates, even with PD pulses of long widths, is also found to be superior. The method is implemented using fixed-point arithmetic leading to faster processing. Hence, the method is suitable for on-site PD measurements. >

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V. Nagesh

Papers

Evaluation of digital filters for rejecting discrete spectral interference in on-site PD measurements