Partial Discharge Pulse Shape Research Articles

A novel automatic pulse segmentation approach and its application in PD-induced electromagnetic wave detection

As one of the most effective detection methods for partial discharge (PD), analysis of the radiated electromagnetic (EM) signals detected by ultra high frequency (UHF) sensor has gained broad attentions. However, the main bottlenecks for this method are probably the massive storage requirements and various noise interferences. Therefore, this paper is focused on investigating a new pulse segmentation technique, which is capable of separating PD pulses from noisy measured data to save the storage space and improve the signal noise ratio (SNR). First, a designed average multi-scale morphological dilate-erode filter (AMMDEF) is developed to obtain the smooth envelope shape of PD pulses. Then, instantaneous phase (IP) of the envelope shape is calculated by the Hilbert Transform (HT) and further smoothed using AMMDEF. Finally, a robust IP based boundary identification criterion (IPBIC) is proposed to accurately extract PD pulses from raw data. Various experiments have been carried out and results show that this method could achieve an average detection error rate (DER) at 0.1375 and an average absolute precision error (APE) at 23.1 ns respectively, even the SNR of signal is as low as 0 dB. Superiority of the developed method over traditional pulse segmentation techniques is also demonstrated.

Partial discharge pulse shape recognition using an inductive loop sensor

Partial discharges (PD) are a clear ageing agent on insulating materials used in high-voltage electrical machines and cables. For this reason, there is increasing interest in measuring this phenomenon in an effort to forecast unexpected failures in electrical equipment. In order to focus on harmful discharges, PD pulse shape analysis is being used as an insulation defect identification technique. In this paper, a simple, inexpensive and high-frequency inductive loop sensor will be used to detect and acquire PD pulses. Several measurements will be made on some controlled test cell geometries in order to characterize PD pulse shapes for different discharge sources. The sensor identification capability has been checked in an insulation system where two simultaneous PD sources were active.

Influence of supply voltage frequency and magnitude on PD pulse parameters

A study on the influence of supply voltage magnitude and frequency on partial discharge (PD) pulse shapes is presented in this paper. Ultrawide band (UWB) time resolved PD pulse measurements were carried out on artificial PD defects using test voltage frequency range of 10-400 Hz and voltage range of 2-11 kV. The manner in which the PD pulse shape responded to changes in the test voltage characteristics depended on the defect type. The observations can be interpreted using the generally agreed theory of PD mechanisms. The knowledge generated in this work is valuable in the interpretation and comparison of PD measurements made at different voltages and frequencies. It also contributes to development of PD defect classification techniques based on the correlation between test voltage frequency and time-resolved PD.

Second generation wavelet transform for data denoising in PD measurement

Detection and diagnosis of partial discharge (PD) activity has been widely adopted in electrical plant condition monitoring. Analysis and detection of PD in practical applications is often hampered by noise in the signal. Recent research has shown that the discrete wavelet transform (DWT) is effective in extracting PD pulses from severe noise. One disadvantage, however, is that DWT does not reproduce accurate PD pulse magnitude and pulse shape after thresholding in the presence of strong noise. This paper presents the application of the second generation wavelet transform (SGWT), as an improved algorithm, to extraction of PD pulse from electrical noise. The paper begins with the description of the fundamental theory and structure of SGWT analysis and comparisons with DWT. The method is then applied to both simulated and real world PD data. Results prove that SGWT can significantly improve the effectiveness of PD denoising.

Partial discharge pulse pattern recognition using an inductive inference algorithm

This paper presents a novel approach in the area of time dependent partial discharge (PD) pulse pattern recognition, to applications based on the inductive learning (decision tree) approach. Different attributes based on pulse shape analysis are used as representative feature vectors that can accurately capture the unique and salient characteristics of the PD pulse shape. In the training phase, a decision tree is developed to relate the pulse shape with the cavity size by using inductive machine learning. The C4.5 machine learning algorithm is deployed to realize the tree using the training data, since it has the capability of inferring the rules and to produce the tree in terms of continuous features. During testing, the cavity size is recognized by means of the rules extracted from the decision tree. The dependency between the features and the classes are examined using the mutual information approach. The proposed algorithm possesses the inherent advantage of explaining the result via the self-created rule base as demonstrated by the results obtained. Those self-created rules can be employed as the basis for applying a fuzzy expert system for the classification of void sizes in an easily interpreted fashion.

Random sampling and data processing for PD-pulse height and shape analysis

A measuring system for the digital acquisition of partial discharge (PD) pulse signals, based on the last generation of oscilloscopes, has been developed in order to perform both PD pulse shape and PD pulse height analysis. Wide-band, fast sampling rate and individually-triggerable memory blocks are available for the acquisition of PD pulse signals. However, a problem of sampling the population of PD pulses arises from the limited available on-line storage memory. Four sampling techniques are investigated and evaluated in order to record an amount of PD pulses which enables PD stochastic inference for a minimum of the on-line memory use. Some statistical indexes based on the pulse-height and pulse-phase distributions are used to compare performances of the different techniques. It is shown that a technique based on the Poisson law provides the most accurate sampling of PD pulses, while minimizing the memory, particularly in the presence of two simultaneously active PD phenomena. The developed procedure enables an accurate analysis of the shape of a large number of PD signals, but also stochastic processing of height distributions, which is becoming a reference for PD pattern analysis.

Novel characterization of PD signals by real-time measurement of pulse parameters

Measurements of partial discharge (PD) are used to examine the insulation characteristics of HV equipment. This test method is also suitable for low-cost components in mass production. To monitor the quality of prefabricated parts, products and production processes, short test cycles and automatic classification are indispensable. In addition, it is desirable to acquire more information about the PD signals for classification. This can be achieved with an ultra wide band (UWB) measurement system. In this paper a new test method, based on analog capture of PD parameters which describe the PD waveform, in addition to the usual parameters, is proposed. The analog values are further processed employing a digital signal processing hardware and a PC. Measurements of frequently encountered PD pulse shapes and their frequency spectra are used to discuss problems which arise from the integration in the frequency domain. These problems are avoided using integration in the time domain, which is employed to determine the apparent charge with the described measurement system. Using the PD pulse duration as an example, measurements on artificially created test samples are presented, which show the influence of the defect dimensions on the PD pulse parameters. This is feasible for test circuit dimensions not extending to a certain value, which depends on the used frequency range of the measurement system.

Neural network system using the multi-layer perceptron technique for the recognition of PD pulse shapes due to cavities and electrical trees

A neural network system, utilizing the multi-layer perceptron approach has been applied to distinguish between power cable insulation partial discharge pulse shapes that are characteristic of cavities and electrical trees. The neural network was found to be capable of recognizing the differences in PD pulses produced by single cavity and electrical tree discharge sources. It also could differentiate between the discharge pulse forms emanating from electrical trees of different lengths; likewise, it was able to recognize changes in the shape of the discharge pulses with time due to aging effects. However, as these recognition capabilities relate only to comparisons of single discharge sources on a one-to-one basis, the application of neural networks to PD pulse shape recognition on actual power cables, where a number of different discharge sources may be discharging simultaneously, is quite premature at this time without more detailed exploratory work on complex discharge patterns. >

Discrimination between PD pulse shapes using different neural network paradigms

A comparison has been carried out on the partial discharge (PD) pulse shape recognition capabilities of neural networks, using the nearest neighbor classifier, learning vector quantization and multilayer perceptron paradigms. The PD pattern recognition capabilities were assessed on artificial cylindrical cavities of different sizes. The performance of the three neural network paradigms was found to be equivalent in all respects, with the exception of the case where a distinction was required between small cavity sizes; under those circumstances, the learning vector quantization paradigm was distinctly superior to the two other paradigms. The experimental results also demonstrated that, even with simple metallic electrode cavities, the discrimination capabilities of the three types of neural networks are not always perfect.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>