Partial Discharge Pulse Waveform Research Articles

A Denoising Method of Partial Discharge Signal Based on Improved SVD-VMD

A denoising method combined with singular value decomposition (SVD) and Variational mode decomposition (VMD) is proposed to eliminate noise in on-site partial discharge (PD) signals from high-voltage electrical equipment. In the Fourier transform power spectrum, periodic narrowband interference was eliminated after SVD was offered to determine the number of singular values of periodic narrowband interference. Then, the PD signal was decomposed into K intrinsic mode function components by VMD. The empirical mode decomposition method was used to determine the K value of white noise’s intrinsic mode function components. An improved 3σ criterion threshold method was proposed to eliminate the residual noise in the PD signal. The denoising method was compared with the other two methods to analyze the denoising effect on the simulation and experimental PD signal. This paper’s denoising method can eliminate periodic narrowband interference and white noise in different PD signals of cavity discharge, corona discharge, and those from the motors’ coil. The denoised PD pulse waveform has a higher signal-to-noise ratio and normalized correlation coefficient and a lower mean square error, indicating that the waveform’s original characteristic remains.

Precision and Accuracy of Pulse Propagation Velocity Measurement in Power Cables

The partial discharge (PD) measurement is an important method used in determining the condition of medium- and high-voltage cable insulation. Considering the propagation velocity of PD signals in power cables is necessary for determining the location of PD defects. However, the determination of velocity is not straightforward due to propagation-related attenuation and dispersion, which distorts the PD pulse waveform. This introduces a degree of uncertainty into the pulse velocity as well as the PD source locations determined based on that velocity, which is usually considered to be of constant value in PD analysis. This paper investigates the accuracy of the pulse propagation velocity measurement in power cables. Tests were performed on a medium voltage power cable in a laboratory setting using two sets of PD-specific measurement equipment: a high-frequency current transformer (HFCT) and an IEC 60270-compliant conventional measurement system. The propagation velocities and their statistical variability were determined using both devices to assess the uncertainty of the propagation velocity measurement. The results indicate that the measured velocity is slightly higher in the case of the HFCT and that the 50% peak threshold value should be used rather than the peak value of PD sensor response waveforms to increase the precision of velocity measurements.

Partial Discharge Localization on Power Cables Based on a Novel Signal Relay System

Time domain reflectometry (TDR) is an effective method used to detect partial discharge (PD) and localize PD sources on power cables. However, for long-length power cables, the attenuation and dispersion of PD signals are significant due to the propagated distance. The attenuation causes a serious reduction of the pulse amplitude, which makes the defects located far away from the PD detector difficult to find. The dispersion causes appreciable asymmetry in the PD pulse wave form. Thus, the localization accuracy is reduced. To solve the problem of long-length cable fault localization, a novel signal relay system is proposed in this paper. Once a weak PD pulse is captured in the signal relay system, another standard Gaussian pulse signal is initiated by a function generator. The Gaussian pulse signal is injected into the cable and travels along the cable. During this procedure, the weak PD pulse is likely to be amplified, and the fault localization accuracy is notably enhanced. To validate the effectiveness of the proposed method, an experimental platform is built in a laboratory, and PD detection of an artificial defective cable is performed. The experimental results show that the signal relay system can effectively “enlarge” the PD pulse on a long-length cable. For long-length power cables, PD detection can achieve a higher localization accuracy based on the proposed method.

Evaluation of Erosion Discharge Characteristics in Inclined Plane Tracking and Erosion Tests on Silicone Rubber under AC and DC Voltages

This paper investigates partial discharge (PD) characteristics that lead to the erosion of silicone rubber (SR) polymer under AC stress and DC stress of both polarities. The experiments are performed on high temperature vulcanized (HTV) SR material samples. The inclined plane test apparatus, constructed in accordance with IEC 60587 requirements, is employed to produce the surface partial discharges and the resulting accelerated aging of the specimens. Two commercial instruments are used to obtain the PD data. A concurrent analysis of visual observations of discharge and PD data is performed to classify discharges based on the severity of material degradation that each type causes. Three types of PD are identified and characterized using diagrams of charge magnitude versus time and a signal processing technique called time-frequency mapping. Individual pulse waveshapes of each type of discharge are also analyzed. PD pulse waveforms are analyzed according to their amplitude, energy, pulse width, and frequency spectrum. These pulse waveform parameters are evaluated and compared for the eroding discharge pulses under AC and DC voltage stresses. It is found that the stages of material degradation during IPT are related to the variations in discharge magnitude and the location of pulse clusters on the time-frequency maps.

Separation and Classification of Concurrent Partial Discharge Signals Using Statistical-Based Feature Analysis

In this paper, an algorithm for feature extraction and classification of high-pressure gas insulation system defects based on statistical analysis of time-domain parameters of partial discharge (PD) signals is presented. The algorithm focuses on the measurement and interpretation of PD signals in the time domain in completion of our previous paper on multiple-source phase resolved patterns. In this procedure, the PD measurements are conducted in different artificial defects that commonly happen in gas-insulated substations (GIS) such as corona, moving particles, floating electrodes, and metallic protrusions. To overcome the noise problem, wavelet transform (WT) technique is applied on the recorded signals. The PD pulse waveform parameters, namely rise time, fall time, slew rate, and pulse width are calculated, investigated and used as the discriminative features to represent each type of PD signals. The separation of PD sources is implemented based on Weibull distribution and K-means unsupervised clustering technique. The higher level statistical based features of each cluster are calculated, studied and used as the inputs of a kernel support vector machine (KSVM) classifier in order to classify multiple PD sources in a robust way. The results of this work demonstrate that the presented probabilistic diagnostic algorithm to extract features from time-domain PD pulse waveforms and their corresponding probabilistic distribution can be employed to cluster and classify PD signals based on their source of origin.

Classification and comparison of AC and DC partial discharges by pulse waveform analysis

This study aims to classify and compare AC and DC partial discharge (PD) based on PD pulse waveform analysis. To achieve this goal, we designed a testbed that enables precise measurements of individual PD pulses. The testbed is used for collecting data from four different types of PDs including cavity, surface, corona, and floating potential discharges generated by individual PD source samples. All samples were examined under AC, positive DC, and negative DC electrical stresses, through which we captured thousands of PD pulses. We classify the waveforms of each PD type into representative groups associated to their discharge mechanisms. The statistical data of the measured pulses are utilized to identify the differences between AC and DC PDs while the clustered patterns of PD amplitude versus their temporal characteristics serve as a means to classify the types of PDs under AC and DC electrical stresses.

Pattern Recognition of DC Partial Discharge on XLPE Cable Based on ADAM-DBN

Pattern recognition of DC partial discharge (PD) receives plenty of attention and recent researches mainly focus on the static characteristics of PD signals. In order to improve the recognition accuracy of DC cable and extract information from PD waveforms, a modified deep belief network (DBN) supervised fine-tuned by the adaptive moment estimation (ADAM) algorithm is proposed to recognize the four typical insulation defects of DC cable according to the PD pulse waveforms. Moreover, the effect of the training sample set size on recognition accuracy is analyzed. Compared with naive Bayes (NB), K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural networks (BPNN), the ADAM-DBN method has higher accuracy on four different defect types due to the excellent ability in terms of the feature extraction of PD pulse waveforms. Moreover, the increase of training sample set size would lead to the increase of recognition accuracy within a certain range.

PD pulse waveform and charge distribution in oil-pressboard insulation with needle-plate model at positive DC voltage: Their characteristics and relationship

A better understanding of the complicated characteristics and interrelationships between the charge distributions and partial discharge (PD) pulse waveforms occurring in oil-pressboard insulation can be used to characterise the PD mechanism as a basis for the optimisation of the insulation structure to minimise PD activity. These characteristics were studied by using a non-inductive resistor to measure the PD pulse waveforms produced in oil-pressboard insulation by a needle-plate at positive DC voltage and through additional simulations based on bipolar charge transport and hydrodynamic drift-diffusion models. It was found that the PD pulse waveforms are unimodal, and with increased applied voltage, their rising and falling edges and equivalent times decrease, and their apparent charge amplitudes and equivalent frequencies increase. These parameters have a saturation state at high voltages (>45 kV) and the oil streamers have voltage-dependent shapes and evolution velocities (vs); the higher the voltages, the higher the values of vs, and the wider are the streamer bodies. Accumulated charge can form a shield layer that hinders streamer evolution. Analysis of the PD mechanism in terms of the relationships between charge distribution and PD pulse waveform reveal that the rising and falling edges of a PD pulse correspond to streamer propagation in the oil gap and charge accumulation at the pressboard surface (i.e., the charging process in the pressboard), respectively. The peak value of the PD pulse waveform is shown to be related to the conduction current in the coupling loop as reflected by the current density at the streamer head.

PD classification in oil-pressboard insulation by simulating a needle-plate model with +DC voltage

The DC voltage related partial discharge (PD) classification in solid dielectrics under a uniform electric field has been studied, but it is more complicated in liquid-solid composite insulation under a highly non-uniform electric field. Therefore, PD pulse waveforms and PD channel shapes in oil-pressboard insulation were investigated by simulating a needle-plate model with a positive DC voltage via pulse current method. The elements of PD can be classified in order of ionizing intensity strength as corona, tree-like PD, single-streamer PD, or multi-streamer PD according to the discharge channel shape. The corona pulses are sharply oscillating, whereas the other types of PD can generate unimodal pulses with no over-zero oscillation. A finite element analysis reveals the PD types are related to the charge quantity at the oil-pressboard interface, which influences the streamer evolution by distorting the space electric field. This study uncovers the physical process during PD in oil-pressboard insulation and provides basis for explaining the PD mechanism.

Recognition of High-Voltage Cable Partial Discharge Signal Based on Adaptive Fuzzy C-Means Clustering

Partial discharge (PD) detection is an effective means to find high-voltage cable defects. However, various interference signals affect the PD signal in practical applications, resulting in wrong judgment. In order to improve the accuracy of PD detection of high voltage cables, an adaptive fuzzy C-means (FCM) clustering was proposed to identify PD signals. The adaptive threshold pulse extraction algorithm based on fixed interval width was employed. The threshold was changed adaptively to extract the effective PD pulse waveform according to the change of the background noise and the degree of PD. Then PD pulse features were analyzed in time and frequency domain by employing the equivalent time frequency analysis method. The adaptive fuzzy clustering algorithm was used to classify the signals. The phase distribution concentration of all kinds of pulse signal was calculated. The phase standard deviation of various types of pulse was taken as the index that measures the concentration of density to distinguish between PD and interference signals. Results show that the adaptive FCM clustering algorithm, compared with the traditional method, can not only identify the PD signal accurately, but also be conscious of the PD category. The PD recognition method proposed in this paper has strong applicability and high accuracy, which is particularly suitable for application in the field of engineering.

Research on Separation of PD Signals Originated from Different Sources in Substation

The existing system of partial discharge location in substation utilizes Omni-directional UHF sensors array which can achieve single-source partial discharge source location. But for multi-source UHF partial discharge signals, the system is inaccurate and can’t distinguish types of partial discharge. This paper proposed to the method multi-source partial discharge signals acquisition and separation based on different characteristics of different types of UHF partial discharge signals in the time domain and frequency domain. Through continuously collected and stored UHF partial discharge pulse waveform, the sampling pulse sequence is analyzed in time-frequency domain to extract characteristic parameters. And then using fuzzy cluster, it can distinguish multi-source UHF partial discharge signals and discharge signals generated by the interference to achieve multi-source UHF partial discharge signals location. And it can lay the foundation of identification of the type of partial discharge. Field tests show that the method is feasible.