Denoising Of Partial Discharge Signals Research Articles

Partial discharge de-noising based on hybrid particle swarm optimization SWT adaptive threshold

A hybrid particle swarm optimization stationary wavelet adaptive threshold denoising (HPO-SWT-ATD) method was proposed to improve the adaptive ability of wavelet transform in partial discharge signal denoising. In the proposed method, the partial discharge signal is decomposed by stationary wavelet transform(SWT) and the multiorder differentiable function based on the Stein unbiased likelihood estimate (SURE) is used as the denoising threshold function. In threshold selection, the improved particle swarm optimization algorithm is used for global adaptive search to select the optimal denoising threshold. The de-noising experiments are carried out on the simulated partial discharge signals and the measured partial discharge signals.The experiment results show that, compared with the hard threshold method, particle swarm optimization threshold method, genetic algorithm optimization threshold method, Wiener filter method and modal decomposition method, the proposed method can better remove the white noise in the PD signal, and the denoising signal distortion degree is smaller.

De-noising of partial discharge ultrasonic signal of insulation bar in large motor based on GMC-wavelet

Abstract In view of the bad operation environment of large motor, which often suffers from various strong noise interference, the partial discharge ultrasonic signal is often annihilated, which makes it difficult to detect and analyse. A de-noising method based on generalized minimax concavity (GMC) and wavelet for partial discharge (PD) ultrasonic signal is proposed. GMC is used to enhance the sparsity of PD ultrasonic signal and eliminate the high-frequency noise signal at the same time. Then the residual high-frequency sparse noise and low-frequency noise of the former are de-noised again combined with wavelet. Finally, the signal is reconstructed to achieve the purpose of de-noising the original PD ultrasonic signal with noise. Compared with ℓ1 -norm method, GMC method, wavelet method and ℓ1 -norm-wavelet method, the simulation results show that based on time domain analysis, the de-noising effect of the proposed method is obviously better than the other four methods. The SNR and MSE of the former are better than those of the latter. In addition, the insulation bar discharge model of large motor is constructed to obtain the actual PD ultrasonic signal, which further verifies its effectiveness, and its de-noising effect is also better than the four methods. This method can not only enhance the sparsity of the target signal and improve the estimation accuracy, but also achieve the de-noising effect, while retaining the effective information of PD ultrasonic signal characteristics. This method can provide new ideas for other types of PD signal de-noising, and lay the foundation for later feature analysis.

A new radial basis function neural network-based method for denoising of partial discharge signals

Partial discharge (PD) assessment is one of the most prevalent techniques in the condition monitoring of high voltage (HV) equipment. In this regard, one of the main stages in the procedure of PD assessment is noise reduction of PD signals. Hence in this paper, a novel artificial neural network (ANN) based denoising method is introduced. Unlike the existing denoising algorithms, the proposed technique treats the noisy PD signal, in a data window, as a set of points that can be estimated by a curve. Hence, radial basis function (RBF)-ANN is utilized being a self-structured algorithm compared to multi-layer perceptron (MLP)-ANN that is influenced by the number of neurons. The performance of the proposed method has been compared with MLP-ANN, wavelet transform (WT) and empirical mode decomposition (EMD) based algorithms for different laboratory generated PD signals suppressed with white noise. The obtained results have proved the superiority of the proposed method.

De-noising of partial discharge signal using wavelet transform for GIS under HVDC

Abstract Detection and analysis of partial discharge (PD) have been regard as the most effective method for condition monitoring and asset management of gas-insulated structures (GIS) in the power system. However, PD detection sensitivity and accuracy are greatly influenced by on-site noise and interference, resulting in failures in PD severity assessment, defect identification or localization. Although de-noising of PD signal under AC was well studied, related investigations under DC have not been carried out. With the rapid development of HVDC technology, it is a new challenge to eliminate noise from PD signal under DC for diagnosis of related power facilities. Therefore, this paper dealt with the discrimination of PD signal based on wavelet transform (WT) techniques for HVDC GIS, aiming to improve the sensitivity and accuracy of insulation diagnosis. Experimental setup was configured to generate PD signal under DC and four types of artificial defects were fabricated to simulate typical insulation defects in GIS. The WT techniques were used to discriminate PD pulse sequences from background noise, amplitude modulation radio interference, non-sinusoidal noise, and switching impulse and the effectiveness was compared with a high-pass filter.

Application of a Novel Wavelet Shrinkage Scheme to Partial Discharge Signal Denoising of Large Generators

Partial Discharge (PD) measurements of large generators are extremely affected and hampered by noise, making the denoising of PD signal an inevitable issue. Wavelet shrinkage is the most commonly employed method for PD signal denoising. The appropriate mother wavelet and decomposition level are critically important for the denoising performance. In consideration of the PD signal characteristics of large generators, a novel wavelet shrinkage scheme for PD signal denoising is presented. In the scheme, a scale dependent wavelet selection method is proposed; the core idea is that the optimum wavelet at each scale is selected as the one maximizing the energy ratio of coefficients beside and inside the range formed by the threshold, which correspond to the signal to be reserved and noise to be removed, respectively. In addition, taking into account the influence of mother wavelet at each scale on the decomposition level, an approach for decomposition level determination is put forward based on the energy composition after decomposition at each scale. The application results on the simulated signals with different SNR obtained by combining the various pulses and measured signal on-site show the effectiveness of the proposed scheme. Besides, the denoising results are compared with that of the existing wavelet selection methods and the proposed wavelet selection method shows an obvious advantage.

Measurement and Denoising of Partial Discharge Signal in High Voltage Cables using Wavelet Transform

A huge amount of exploration propagated over the past decade investigates the characterization of Partial Discharge (PD) inception in cable ideology. Underground cables are passed down as surrogate for over hauling in congested areas. The intention of this research is to examine the feasibility of exploring insulation defects present in High Voltage (HV) Cable setup by employing PD disclosure under alternating current (AC) Voltage. Study of PD characteristics has a congregate of predictable distinguished contraption to prove the probity and the affirmation of electrical insulation of Power System. In this work, the cable is exposed into the measurement of PD signal under artificially conceived defects. PD signal parameters are mainly depends on the size of void and applied voltage. In general, the measured PD signal is depraved with interferences. To identify the exact characteristics of PD distinctive and its severity, the PD signal is subjected to Wavelet Transform (WT) for denoising. Different types of WT families with various level is used for de-noising. To identify the effectiveness of the WT for de-noising guidelines like Signal to Reconstruction Error Ratio (SRER) and Reduction in Noise Level (RNL) are used.

Partial discharge signal denoising by using hard threshold and soft threshold methods and wavelet transformation

The important thing to do after we detect the existence of partial discharges inside an isolation system is signal processing. One form of signal processing that needs to be carried out on the generated waveform is denoising or noise removal from the wave obtained. In this study, an experiment will be conducted to denounce partial discharge waves using Wavelet and Threshold transformations. Wavelet transformation is applied in various levels then we compare the best denomination while denoisation using Threshold is applied using Soft Threshold and Hard Threshold.

Partial Discharge Signal Denoising by Discrete Wavelet Transformation

There are several techniques to eliminate noise on signals such as signal analysis in time zone, frequency area and Short Time Fourier Transform. In the partial discharge signal, the signal analysis can be realized in the time domain or in the frequency domain. Where frequency and time analysis is the basic methodology in signal processing. The disadvantage of signal processing in the frequency domain is the time domain information being lost. Though, we need another solution for signal analysis. Wavelet transform is a solution to overcome the shortage because it can provide information in time and frequency domain. Wavelet transforms (WT) have been applied in various fields for signal processing in recent years. In this paper, a study of wavelet transformation was done to reduce noise from partial discharge signals that has been obtained from previous experiment. This application uses matlab software. In simulation, this wavelet application software performed as much as 144 times for one partial discharge signal by combining threshold, wavelet type, and level

Partial discharge signal self-adaptive sparse decomposition noise abatement based on spectral kurtosis and S-transform

PurposeNoise abatement is one of the key techniques for Partial Discharge (PD) on-line measurement and monitoring. However, how to enhance the efficiency of PD signal noise suppression is a challenging work. Hence, this study aims to improve the efficiency of PD signal noise abatement.Design/methodology/approachIn this approach, the time–frequency characteristics of PD signal had been obtained based on fast kurtogram and S-transform time–frequency spectrum, and these characteristics were used to optimize the parameters for the signal matching over-complete dictionary. Subsequently, a self-adaptive selection of matching atoms was realized when using Matching Pursuit (MP) to analyze PD signals, which leading to seldom noise signal element was represented in sparse decomposition.FindingsThe de-noising of PD signals was achieved efficiently. Simulation and experimental results show that the proposed method has good adaptability and significant noise abatement effect compared with Empirical Mode Decomposition, Wavelet Threshold and global signal sparse decomposition of MP.Originality/valueA self-adaptive noise abatement method was proposed to improve the efficiency of PD signal noise suppression based on the signal sparse representation and its MP algorithm, which is significant to on-line PD measurement.

Wavelet denoising of partial discharge signals and their pattern classification using artificial neural networks and support vector machines

Este artículo presenta dos enfoques de reconocimiento de patrones usando huellas dactilares de descargas parciales como características de entrada para llevar a cabo la clasificación de patrones de DP. Un perceptrón multicapa (MLP) basado en el algoritmo de propagación hacia atrás y una máquina de soporte vectorial fueron entrenados para reconocer tres tipos de patrones de DP. Los resultados experimentales demostraron que los algoritmos pueden arrojar altas tasas de reconocimiento. De otra parte, la trasformada wavelet discreta (DWT) fue utilizada para eliminar el nivel de ruido presente en las DP como una etapa previa al proceso de clasificación. Diferentes wavelets madre fueron probadas a diferentes niveles de descomposición con el objeto de encontrar parámetros wavelet apropiados para obtener una mejor relación señal-ruido (SNR) y menos distorsión después del proceso de filtrado.

Energy conservation‐based thresholding for effective wavelet denoising of partial discharge signals

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.

Impulsive noise reduction for transient Earth voltage‐based partial discharge using Wavelet‐entropy

Partial discharge (PD) is caused by the localised electrical field intensification in insulating materials. Early detection and accurate measurement of PD are very important for preventing premature failure of the insulating material. Detection of PDs in metal-clad apparatus through the transient Earth voltage method is a promising approach in non-intrusive on-line tests. However, the electrical interference from background environment remains the major barrier to improving its measurement accuracy. In this study, a wavelet-entropy-based PD de-noising method has been proposed. The unique features of PD are characterised by combining wavelet analysis that reveals the local features and entropy that measures the disorder. With such features, a feed-forward back-propagation artificial neural network is adopted to recognise the actual PDs from noisy background. Comparing with other methods such as the energy-based method and the similarity-comparing method, the proposed wavelet-entropy-based method is more effective in PD signal de-noising.

Automatic Blind Equalization and Thresholding for Partial Discharge Measurement in Power Transformer

Partial discharge (PD) signals acquired from on-line measurements of power transformers are easily overwhelmed by various interference and noise. This paper proposes an automatic blind equalization (BE) and morphological thresholding method for PD signal de-noising. Firstly, BE automatically selects an equalized signal that reveals PD impulses from an acquired noise-corrupted signal. Then, automatic morphological thresholding (AMT) is adopted for determining thresholds on the equalized signal. After de-noising with BE and AMT, phase-resolved pulse sequence (PRPS) is constructed and used for analyzing the types of insulation defects that cause discharges. To verify the proposed method, PD measurements on experimental PD models and a distribution transformer have been conducted. The results show that PD impulses can be extracted from severely noise-corrupted signals by using the proposed method. Also, PRPS constructed from de-noised signals can achieve consistency in revealing the types of insulation defects even different types of PD sensors and measurement systems are used.

Research of an Improved Wavelet Threshold Denoising Method for Transformer Partial Discharge Signal

In order to overcome the discontinuance of the hard thresholding function and the defect of seriously slashing singularity in the soft thresholding function, improve the denoising effect and detect the transformer partial discharge signal more accurately, in this paper an improved wavelet threshold denoising method is put forward through analyzing the interference noise of transformer partial discharge signals and studying various wavelet threshold denoising method, especially the wavelet threshold denoising method that overcomes the shortcomings of the hard and soft threshold. Simulation results show that the denoising effect of this method has been greatly improved than the traditional hard and soft threshold method. This method can be widely used in practical transformer partial discharge signal denoising.

Feature-oriented de-noising of partial discharge signals employing mathematical morphology filters

Noise reduction is one of the important tasks of cable insulation assessment based on partial discharge. This paper has developed a novel attitude in noise reduction, i.e. feature-oriented de-noising. Implementing this idea, a new de-noising filter based on Mathematical Morphology Filters (MMF) is presented where the process of removing white noise mixed with partial discharge signal is executed by employing several simple morphological filters. Comparing with the de-noising process based on Wavelet-transform, in terms of extracted features after de-noising, the proposed method preserves the features of PD pulses better. The performance evaluations and comparisons are examined by simulated and field measured signals.

An Improved Wavelet Threshold Denoising Method for Transformer Partial Discharge Signal

In order to overcome the discontinuance of the hard thresholding function and the defect of slashing singularity more seriously in the soft thresholding function, and improve the denoising effect and detect the transformer partial discharge signal more accurately, this paper puts forward an improved wavelet threshold denoising method through analyzing the interference noise of transformer partial discharge signals and studying various wavelet threshold denoising method, especially the wavelet threshold denoising method that overcomes the shortcomings of the hard and soft threshold. Simulation results show that the denoising effect of the method has been greatly improved than the traditional hard and soft threshold method. This method can be widely used in practical transformer partial discharge signal denoising.

Research Progress of Wavelet Denoising Method of Transformer Partial Discharge Signal

Wavelet analysis has been widely used in the denoising of partial discharge signal of transformer. This paper introduces the main method of partial discharge signal denoising, which focuses on the studying of wavelet denoising methods. The main wavelet denoising methods are introduced herein including wavelet decomposition and reconstruction method, wavelet thresholding method, the translation invariant wavelet thresholding method, the wavelet denoising based on modulus maxima method, and the most widely used wavelet thresholding is introduced primarily. The analysis of their advantages and disadvantages is helpful to choose a proper wavelet denoising method.

Wavelet de-noising of partial discharge signals based on genetic adaptive threshold estimation

Wavelet shrinkage is efficient for de-noising the partial discharge (PD) detection. An improved wavelet de-noising approach for PD online measurement is presented. The wavelet de-noising approach is based on a genetic adaptive threshold estimation (GATE) scheme. The thresholding functions with continuous derivatives are used for the GATE scheme. A genetic algorithm is used to obtain global optimum thresholds of the GATE, and to improve the robustness and computation speed of the adaptive threshold estimation. De-noising experiments of simulative high-frequency PD signals, actual PD ultra-high-frequency (UHF) signals, and a field detected PD signal are presented. The GATE generates significantly smaller waveform distortion and magnitude errors than the Donoho's soft threshold estimation.

Signal denoising techniques for partial discharge measurements

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.

Denoising of partial discharge signals in wavelet packets domain

Despite advances in the filtering techniques of partial discharge (PD) signals, an acceptable signal to noise ratio (SNR) remains elusive due to the presence of environmental noises. A wavelet packet transform-based method is proposed for effective denoising of on-site partial discharge data from a power generator and a gas-insulated substation. Taking advantage of the statistical properties of white noise, the method uses variance as the criterion to select the best tree. It is shown by comparative studies that the method restores the PD pulse from data with very low SNR. The denoised PD pulses enable more accurate diagnosis of insulation defects in HV equipment.