Different partial discharge (PD) detection and measurement procedures suitable for use on cables, capacitors, transformers and rotating machines are examined and compared. Both narrow and wide bandwidth PD detectors are considered; particular attention is given in regard to their suitability to different types of electrical apparatus and cable specimens under test as well as their applicability to discharge site location and their capability to detect different forms of PD. A rather substantial portion of the discussion is devoted to the use of intelligent machines as applied to PD pattern recognition in terms of either PD pulse-height/discharge epoch (phase) distributions or discharge pulse shape attributes.

Partial discharges. Their mechanism, detection and measurement

Increasing interest has been seen in condition monitoring (CM) techniques for electrical equipment, mainly including transformer, generator, and induction motor in power plants, because CM has the potential to reduce operating costs, enhance the reliability of operation, and improve power supply and service to customers. Literature is accumulated on developing intelligent CM systems with advanced practicability, sensitivity, reliability, and automation. A literature survey is felt necessary with an aim to reflect the state-of-the-art development in this important area. After introducing the concepts and functions of CM, this paper describes the popular monitoring methods for and research status of CM on transformers, generators, and induction motors, respectively. The paper also points out the potential benefits through the utilization of advanced signal processing and artificial intelligence techniques in developing novel CM schemes.

Condition monitoring techniques for electrical equipment-a literature survey

This paper deals with digital acquisition, classification and analysis of the stochastic features of random pulse signals generated by partial discharge (PD) phenomena. Focus is made on a new measuring system for the digital acquisition of PD-pulse signals, which operates at a sampling rate high enough to avoid the frequency aliasing, but that provides an amount of PD pulses which enables PD stochastic analysis. A separation and classification method, based on a fuzzy classifier, is developed for the analysis of the acquired PD-pulse shape signals. The result of the fuzzy classification is a cluster of signals homogeneous in terms of stochastic features of PD pulses. The classification efficiency is evaluated resorting to the PD-pulse height and phase distributions analysis. The instrumentation, and the associated classification methodology, are applied to measure and analyze PD data recorded for mica-insulated stator bars and coils, where typical defects, occurring during normal operations, were simulated. It is shown that the proposed procedure enables PD-source identification to solve the identification problems which arise, in particular, when different sources of PD are simultaneously active. In addition fuzzy classification provides an efficient noise-rejection tool.

Digital detection and fuzzy classification of partial discharge signals

Partial discharge (PD) measurement has long been used as a test to evaluate different insulation system designs, and as a quality control test for new equipment. However, in the past 20 years, PD measurement has been widely applied to diagnose the condition of the electrical insulation in operating apparatus such as switchgear, transformers, cables, as well as motor and generator stator windings. Improvements in the capabilities as well the lower cost of sensors, electronics and memory is partly the reason for the increased popularity of PD diagnostics. Another reason has been the development of methods-including the use of ultrawide band detection-to improve the reliability of the PD measurement in the presence of noise. In addition, rapid progress is being made in automated pattern recognition techniques that also helps to suppress noise. This paper reviews the various PD measurement technologies that have been specifically developed to improve PD diagnostic methods, and outlines how they have been implemented for stators, cables, transformers and switchgear. Areas for further research are also presented.

Partial discharge diagnostics and electrical equipment insulation condition assessment

This paper presents a signal analysis technique for machine health monitoring based on the Hilbert-Huang Transform (HHT). The HHT represents a time-dependent series in a two-dimensional (2-D) time-frequency domain by extracting instantaneous frequency components within the signal through an Empirical Mode Decomposition (EMD) process. The analytical background of the HHT is introduced, based on a synthetic analytic signal, and its effectiveness is experimentally evaluated using vibration signals measured on a test bearing. The results demonstrate that HHT is suited for capturing transient events in dynamic systems such as the propagation of structural defects in a rolling bearing, thus providing a viable signal processing tool for machine health monitoring

Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring

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

Fractals have been used extensively to provide a description and to model mathematically many of the naturally occurring sampler shapes, such as coastlines, mountain ranges, clouds, etc., and have also received increased attention in the field of image processing, for purposes of segmentation and recognition of regions and objects present in natural scenes. Among the numerous fractal features that could be defined and used for an image surface, fractal dimension and lacunarity have been found to be useful for recognition purposes. Partial discharges (PD) occurring in all HV insulation systems is a very complex phenomenon, and more so are the shapes of the various 3-d patterns obtained during routine tests and measurements. It has been fairly well established that these pattern shapes and underlying defects causing PD have a 1:1 correspondence, and therefore methods to describe and quantify these pattern shapes must be explored, before recognition systems based on them could be developed. This contribution reports preliminary results of such a study, wherein the 3-d PD pattern surface was considered to be a fractal, and the computed fractal features (fractal dimension and lacunarity) were analyzed and found to possess fairly reasonable pattern discriminating abilities. This new approach appears promising, and further research is essential before any long-term predictions can be made. >

Can fractal features be used for recognizing 3-d partial discharge patterns

Multi-resolution signal decomposition (MSD) technique of wavelet transforms has interesting properties of capturing the embedded horizontal, vertical and diagonal variations within an image in a separable form. This feature was exploited to identify individual partial discharge (PD) sources present in multi-source PD patterns, usually encountered during practical PD measurements. Employing the Daubechies wavelet, features were extracted from the third level decomposed and reconstructed horizontal and vertical component images. These features were found to contain the necessary discriminating information corresponding to the individual PD sources. Suitability of these extracted features for classification was further verified using a radial basis function neural network (NN). Successful recognition was achieved, even when the constituent sources produced partially and fully overlapping patterns, thus demonstrating the applicability of the proposed novel approach for the task of multi-source PD classification.

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Wavelet analysis for classification of multi-source PD patterns

A novel approach using the short-time. Fourier transform and wavelet transform (time-frequency analysis tools) for fault detection during impulse testing of power transformers is described. The neutral and/or capacitively transferred currents which are recorded during an impulse test can be directly analysed with this approach. These currents are considered to be evolving in time, i.e. as nonstationary signals, especially when there is a fault. Results from simulation studies are presented wherein the fault condition is modelled as a fast decaying transient superposed on the neutral current. A comparison of the two transforms is made to assess their ability to detect as small a fault as possible and other implemenational issues. Advantages of these methods over the conventional transfer function method are demonstrated, and it appears that the wavelet transform is better suited for this task.

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Short-time Fourier and wavelet transforms for fault detection in power transformers during impulse tests

Detection of major faults in power transformers during impulse tests has never been an issue, but is rather difficult when only a minor fault, say a sparkover between adjacent coils or turns and lasting for a few microseconds, occurs. However, detection of such a type of fault is very important to avoid any catastrophic situation. In this paper, the authors propose a new and powerful method capable of detecting minor incipient faults. The approach is based on wavelet transform analysis, particularly the dyadic-orthonormal wavelet transform. The key idea underlying the approach is to decompose a given faulty neutral current response into other signals which represent a smoothed and detailed version of the original. The decomposition is performed by the multiresolution signal decomposition technique. Preliminary simulation work demonstrated here shows that the proposed method is robust and far superior to other existing methods to resolve such types of faults.

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L. Satish

Papers

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

Can fractal features be used for recognizing 3-d partial discharge patterns

Wavelet analysis for classification of multi-source PD patterns

Short-time Fourier and wavelet transforms for fault detection in power transformers during impulse tests

Multiresolution signal decomposition: a new tool for fault detection in power transformers during impulse tests