Extract Partial Discharge signal using wavelet for on-line measurement

Wavelet-based de-noising of on-line PD signals captured by Pearson coil in covered-conductor overhead distribution networks

Recent studies have shown that wavelet transform can effectively be used for noise reduction in the context of partial discharge (PD) signal detection and classification. Several thresholding approaches for wavelet denoising have been reported in the literature. In this study, a novel wavelet threshold estimation method, named energy conservation‐based thresholding (ECBT), is introduced. The proposed thresholding function is capable of conserving a significant portion of the original signal energy, while the threshold value is determined based on the relative difference between the original and noisy signal energies. The proposed method is first applied to PD signals contaminated with different levels of simulated noise. Results show that ECBT produces a denoised PD signal with higher signal‐to‐noise ratio (SNR) and less distortion than PDs produced by the existing wavelet methods. Then, ECBT is modified to address actual PD signals corrupted with real noise, where a robust SNR estimation method is derived to estimate the noise level embedded in the measured PD signals. The denoised PD signals indicate that the proposed method yields higher reduction in noise levels than other methods.

Partial discharge (PD) online measurement is considered as one of the most effective means to assess stator insulation state and prevent sudden accidents of hydro-generator. However, the existence of external interferences has greatly reduced the credibility of PD online measurement technology of hydro-generators. In this paper, a set of high reliable hydro-generator PD on-site measurement system was developed, which made use of PDA technique to obtain PD pulses, and in order to eliminate the external interferences and ensure the reliability of PD pulses acquisition, the combination of directional double sensors and pulse time-delay technique are adopted, which can effectively suppress the external pulse shaped interferences (PSI) from power network. At present, the system has been applied to several hydro-generators in a huge-size hydropower plant to assess the stator insulation state, and a lot of on-site measurement results were achieved and explained. An actual failure of the stator insulation was checked out by the system and a great deal of economic loss was avoided, which greatly increases the credibility of the PD online monitoring technology.

One of the major difficulties of online partial discharge (PD) measurement of transformers is the effective extraction of PD signals, which are heavily coupled with noise. Wavelet transform has been widely adopted for the extraction of PD signals. However, its performance is highly dependent on proper selection of mother wavelet, decomposition levels and thresholding method. Such selection involves a series of complicated and extensive computations. Moreover, it is also difficult to find a mother wavelet, which has a high degree of similarity to PD signals generated by different types of PD sources. This paper proposes a novel differential signal extraction method as an alternative to wavelet transform for PD signal extraction. Its implementation is straight-forward as it does not involve any mother wavelet selection and decomposition level determination. It can effectively extract PD signals in the presence of multiple PD sources. The applicability of the proposed method is verified using datasets obtained from experiments in the laboratory and measurements on in-service transformers at substations. Copyright © 2015 John Wiley & Sons, Ltd.

Today the sensitive measurement of partial discharge (PD) signals in XLPE cable can be an important element for quality assurance and diagnostics in the factory and during the on-site test procedure. The results clearly indicate that PD analysis of XLPE cable is an excellent online tool to assess its dielectric condition. However, as the serious interference in-field, the acquired PD signals may be interrupted by periodic interferences and white noises, easily. This paper describes an intelligent morphological filter algorithm to remove the periodic pulse interference and white noise contained in the PD signals. The algorithm consists of two stages of morphological processing. The performance of the algorithm is evaluated with simulated and real PD signals. Compared with classical wavelet, there are substantial improvements in the indexes of average error, root mean square (rms) error and maximum error, especially in reducing distortion of the baseline waveform.

On-site and on-line partial discharge (PD) measurements are well recognized as difficult tasks due to the large scale and diversity of interferences usually encountered in high voltage facilities. Several techniques have been proposed in literature to improve test conditions on the field, based either on analog and digital approaches. More recently, wavelets and their derivatives have shown to be very effective for the processing of PD signals due to their potential to identify transient, time-localized signals. This paper presents the development and results of a new technique to denoise PD signals based on data mining concepts applied to Undecimated Wavelet Transform (UWT) modulus maxima lines propagation. The theory of modulus maxima lines is introduced and their use is justified as an effective way to identify and localize PD pulses. The UWT was employed as a way to increase robustness against the effects of decimation, which is a characteristic of the orthogonal Wavelet Transform that causes random losses of PD pulses. We took advantage of the improved UWT capability to recover signals by using a modified version of the cycle spinning approach. Denoising was performed by separating the maxima lines related to PDs from those related to noise, followed by reconstruction using the inverse UWT. Separation was achieved by the use of data mining tools. The performances of several algorithms were evaluated and compared, including investigations regarding statistically divergent noise types, effects of data pre-processing like normalization and cleansing, procedures for training and comparisons of supervised and unsupervised techniques. Results obtained for both simulated and measured PD signals confirm that the approach is superior when compared to standard linear filters and non-linear wavelet-based techniques. This is particularly noticeable when processing non-stationary time-localized interferences, a situation in which these techniques fail completely.

Optimal selection of features of Partial Discharge (PD) signals recorded from defects in High Voltage (HV) cables will contribute not only to the improvement of PD pattern recognition accuracy and efficiency but also to PD parameter visualization in HV cable condition monitoring and diagnostics. This paper presents a novel Random Forest (RF)-based feature-selection algorithm for PD pattern recognition of HV cables. The algorithm is applied to feature selection of both PD signals and interference signals with the aim of obtaining the optimal features for data processing. First, the experimental data acquisition and feature extraction processes are introduced. PD signals were captured from faults created in a cable to obtain the raw PD data, and then, a set of 3500 transient PD pulses and a set of 3500 typical interference pulses were extracted, based on which 34 PD features were extracted for further processing. Furthermore, 119 two-dimensional features and 1082 three-dimensional features were generated. The paper then discusses the basic principle of the RF algorithm. Finally, RF-based feature selection was implemented to determine the optimal features for PD pattern recognition. The results were obtained and evaluated with the Back Propagation Neural Network and Support Vector Machine. Results show that the proposed RF-based method is effective for PD feature selection of HV cables with the potential for application to additional HV power apparatus.

This article aims to study the pattern of partial discharge (PD) signals occurring on the insulators of high-voltage systems. A mother wavelet comparison and wavelet decomposition are presented to detect and locate PD signals by dividing them into three processes: 1) Signal test generation, employing RC and RLC impedance circuits whereby the output voltage pulses in the RC impedance circuit are expressed as damped exponential pulses (DEPs) and those of the RLC as damped oscillatory pulses (DOPs). White Gaussian noise (AWGN) is then added, which mimics the effect of many random processes in measurement systems. The concept involves applying noise to the original signal and removing it with wavelet transform using various wavelet templates such as Daubechies, Coiflet, Symlet, and biorthogonal to separate the signal components. The experiment results are then compared, and a performance evaluation performed using mean square error (MSE) as in the first two signals. 2) DEPs and DOPs are added with a sine wave to simulate a virtual measurement from a measuring instrument according to the superposition principle using a band-pass filter with the frequency range specified by the two elements to determine the frequency of the resulting PD. 3). The results of the PD signal experiments developed in the laboratory are also evaluated for efficiency by subjective measurement by a PD signal specialist. Therefore, partial discharge denoizing is evaluated using the mother wavelet as a preprocessing step for feature extraction and classification.

New online partial discharge (PD) measuring techniques for the assessment and monitoring of the insulation in large rotating machines are introduced. These nonintrusive and flexible techniques use purpose-built high-voltage high-frequency current transformers (HVHFCT). The PD signal from the CT is sent directly to a digital oscilloscope for measurement and analysis. The HVHFCT is also suitable for live-line PD measurement, in which the CT is temporarily placed outside the machine, greatly simplifying PD measurement. The CT characteristics and their applications are presented and discussed. The authors have also investigated an online or live-line PD calibration method for quantitative PD measurement. Thereby, even the first PD measurements from different machines can be compared. The calibrator can also be used to evaluate the performance of existing online PD monitoring devices. The calibration methods, and their performance, are described and discussed.

Partial discharge (PD) signals generated by defects in a transformer insulation can be captured through measurement instruments and they may be used, after preprocessing, to discriminate the PD Sources. Some of the artificial defect models, such as: corona, internal cavity and surface discharge in air are developed in the laboratory. These defect models are put in parallel under a high voltage stress. The PD signals stemmed from these sets of multiple PD sources are captured. In this paper six bandpass Alters (with two MPD 600 devices) are used for feature extraction of these signals. For PD signals discrimination, the Density-Based Spatial Clustering of Applications with Noise density (DBSCAN) method is employed with two freedom parameters which resulted in an accurate discrimination of types of partial discharge sources.

Partial Discharge (PD) signal measurement is very significant tool in analyzing condition of the electrical insulation. The PD information is lost in the presence of various noises. The wavelet transform (WT) based denoising provides a better platform for pre and post processing of PD signal. The wavelet adaptive Thresholding de-noising techniques are well suited for reducing the noise. This paper adopts the various adaptive thresholding techniques such as VisuShrink, SureShrink, combination of the two called Heursure, minimax thresholding and BayesShrink, which are broadly classified as Global and Local thresholding methods. The algorithm presents the comparative analysis for the selection of optimal mother wavelet. Once the optimal mother wavelet is chosen, selection of the best thresholding rule is identified by comparing the values of signal to noise ratio (SNR), mean square error (MSE) and Peak Signal to Noise ratio (PSNR) of all the techniques. The algorithm also presents the comparison between Hard and Soft thresholding. It is shown that the soft thresholding is best suited to remove the noise compared to hard thresholding. The simulated Damped Exponential Pulse (DEP) and Damped Oscillatory Pulse (DOP) has been used. Three sets of PD data are considered to check the performance of the algorithm.

This paper proposes a method of partial discharge (PD) signal denoising. First, the preset parameters of variational mode decomposition (VMD) are optimized based on particle swarm optimization (PSO) algorithm. The optimized VMD is used to decompose the denoised discharge signal into different modal components, and the effective components are screened according to kurtosis. Finally, hard threshold wavelet transform (WT) is used to remove residual noise for each effective component, and the denoised signal is obtained by superposition of each effective component. Discharge pulse 3 and discharge pulse 4 only have fuzzy waveform profiles. Discharge pulse 1 has slight waveform fluctuations at the maximum discharge amplitude, and discharge pulse 2 is almost completely overwhelmed by the background noise rendering it unrecognizable.

Partial discharge (PD) measurement is an efficient method for monitoring the integrity of the high‐voltage equipment. However, extensive interferences can compromise the measured PD signals. After the acquisition, a post‐processing algorithm is required for the effective extraction of PD signals from noise. The wavelet shrinkage is one of the most popular tools used in such application. The wavelet processing involves the choice of parameters that will determine the performance of denoising. With the purpose of finding suitable parameters, optimisation techniques, such as genetic algorithms (GAs), can be used in conjunction with a fitness function to evaluate how good a solution is. This work proposes a new fitness function, based on a composition of local variables and statistics usually employed for the evaluation of PD denoising algorithms. The proposed function was applied to a set of PD signals and proved effective in the identification of optimal parameter values by a GA, thereby producing better PD denoising.

The cable attachment interface has always been a weak insulation link in cable systems. In this paper, the growth characteristics of electric tree at the interface was studied at 70 °C, and the growth morphology and partial discharge (PD) signals of electric trees were collected simultaneously. The cross-linked polyethylene (XLPE) and silicone rubber (SIR) were pressed together by a pressing device, and AC high pressure was applied to a steel needle that is vertically inserted 1mm from the XLPE-SIR interface, with a pin plate spacing of 2mm. It was shown that there was a growth pattern of "trigger-rapid growth-stagnation-breakdown" in the electric tree at the interface. It was found that the amplitude and frequency of partial discharge are closely related to the growth stage of electric tree. The relationship between the growth characteristics of electrical tree and the partial discharge signal at the interface was established, which provided the experimental basis for the online monitoring technology to judge the insulation state of cable accessories more accurately.

Partial Discharge (PD) detection in High Voltage (HV) equipment is an essential technique for assessing the insulation condition in the power system apparatus. The presence of PD is one of the critical indications for the defect and deterioration of the insulating material. Effective signal processing techniques are required to acquire, analyse, and classify PD signals into a specific fault type. The PD measurement device is prone to be affected by various noise sources, which leads to misinterpretation of the PD signal. This research focuses on developing a suitable algorithm for denoising PD signals and proposes a novel feature extraction process for classifying PD signals. The signal decomposition-based denoising algorithms in various engineering disciplines have become popular, and such techniques are being applied to PD signals. A novel PD signal denoising using adaptive decomposition, optimized by entropy and statistical parameters is proposed. Furthermore, a novel entropy-driven PD signal classification method is proposed. Initially, the PD signal is decomposed using various adaptive decomposition methods to get Intrinsic Mode Functions (IMFs). Then, IMFs are analysed using statistical or information theory measures such as Kurtosis, Mutual Information (MI) and Dispersion Entropy (DE). The IMFs are classified as noise, noise–dominant and signal IMFs, based on Kurtosis and MI values. The DE value of IMFs is used to select the regularisation parameter of Group-Sparse Total Variation (GSTV). The noise–dominant IMFs are subjected to the GSTV method, and the signal IMFs are added to the output vector to reconstruct the denoised output. The noisy IMFs are discarded for further processing. The proposed methods are applied under different input signal-noise ratios, lengths, and noise sources. The proposed denoising methods with the results and findings are presented and discussed, along with the performance metrics related to the filters. In addition, Tsallis entropy and a novel Generalized Gaussian Distribution – Refined Composite Multiscale Dispersion Entropy (GGD–RCMDE) based features are proposed for the time series signal. The suggested feature extraction methods are applied to the PD dataset to extract the features from various PD signals such as ‘corona’, ‘surface’ and ‘void’. A novel preprocessing method followed by the min-redundancy max-relevance (mRMR) feature selection method is applied to select sensitive features. A multiclass classifier model is trained using the most sensitive features to classify PD faults. The robust experimental analysis of the feature extraction method was carried out and reported. The performance metrics are computed using the results obtained through the experiments. The effectiveness of the proposed denoising and classification methods was presented with a suitable discussion and conclusion.