Partial Discharge Pulses Research Articles

Инновационные решения для измерения частичных разрядов

The article discusses systems developed using an innovative approach to measuring partial discharges, their advantages over traditional methods of monitoring cable lines and electrical equipment, as well as the principles of their operation. The design of the systems is based on a special broadband sensor-antenna which provides remote recording of both partial discharge pulses and the phase of alternating current flowing in the object of measurement without taking the equipment under test out of operation. Due to the wireless nature and system compact dimensions, partial discharge measurements can perform in previously inaccessible conditions, significantly expanding the capabilities of this diagnostic method. The partial discharge measurements are an up-to-date method for predictive diagnostics of cable systems and are necessary for the condition-based maintenance.

Towards Secured Online Monitoring for Digitalized GIS Against Cyber-Attacks Based on IoT and Machine Learning

Recently, the Internet of Things (IoT) has an important role in the growth and development of digitalized electric power stations while offering ambitious opportunities, specifically real-time monitoring and cybersecurity. In this regard, this paper introduces a novel IoT architecture for the online monitoring of the gas-insulated switchgear (GIS) status instead of the traditional observation methods. The proposed IoT architecture is derived from the concept of the cyber-physic system (CPS) in Industry 4.0. However, the cyber-attacks and the classification of the GIS insulation defects represent the main challenges against the implementation of IoT topology for the online monitoring and tracking of the GIS status. For this purpose, advanced machine learning techniques are utilized to detect cyber-attacks to conduct the paradigm and verification. Different test scenarios on various defects in GIS are performed to demonstrate the effectiveness of the proposed IoT architecture. Partial discharge pulse sequence features are extracted for each defect to represent the inputs for IoT architecture. The results confirm that the proposed IoT architecture based on the machine learning technique, that is the extreme gradient boosting (XGBoost), can visualize all defects in the GIS with different alarms, besides showing the cyber-attacks on the networks effectively. Furthermore, the defects of GIS and the fake data due to the cyber-attacks are recognized and presented on the dashboard of the proposed IoT platform with high accuracy and more clarified visualization to enhance the decision-making about the GIS status.

Comparing the New Improved RLC and CMTL Models for Measuring Partial Discharge in Transformer Winding

Nowadays, one of the most well-known methods for accurate simulation of the fast-transient phenomenon as well as the study and location of partial discharge (PD) in transformer windings is the method based on winding modeling. In this regard, the circular multi-conductor transmission line (CMTL) model, which is an improved model of the MTL model, has been proposed as the finest solution since it is acceptable in an extensive frequency range and it can investigate the effect of winding rotation by considering the radius and angle variables. However, one of the fundamental problems of using the CMTL model is that a set of large equations, known as complete admittance matrices, must be solved for all winding turns, which typically contain thousands of turns. To overcome this problem, we presented an improved RLC model, which has the same accuracy as the CMTL model in terms of frequency validity range and less computational complexity. In this model, the variables of length and curvature of the turns are also considered in the formulations. Although this method is theoretically accurate, its reliability method must also be determined in practice and how the PD signal propagation must be carefully considered due to the problems in high-frequency modeling of the winding. Numerical analyses and laboratory measurements resulting from the use of PD pulses on a 20 kV winding of a distribution transformer, as well as a 63 kV, 30 MV winding, revealed that a valid frequency range is extendable up to 5 MHz for the proposed improved model without a significant increase in computational complexity.

Partial Discharge Electrical Tree Growth Identification by Means of Waveform Source Separation Techniques

Electrical trees are one of the main degradation processes leading to failure of high voltage polymeric insulation. Electrical trees grow under the effect of partial discharges (PD), which can be measured and analyzed for condition monitoring of electrical insulation. In this paper, techniques that are normally used for classification of PD and noise separation were explored in their ability to determine the stage of growth of electrical trees: Spectral Power Clustering Technique (SPCT), Time-Frequency (TF) maps and Chromatic Technique (CT). The techniques allowed to analyze PD signals captured in ultra-high frequency (UHF) range with an antenna during tree growth. Laboratory treeing-samples were made of epoxy resin and trees were generated at six different excitation frequencies: 0.1, 10, 50, 150, 250 and 350 Hz. The results showed that two parameters, part of SPCT and TF maps, were sensitive to the tree progression and showed a consistent relation with the length of the tree: the low-frequency power ratio and the equivalent bandwidth. These two parameters were selected to create a new map, proposed for the characterization of electrical tree growth, which is more consistent and robust than the original separation maps. It was found that the low-frequency content of PD pulses proportionally increased with tree propagation.

Partial Discharge Location Identification Using Permutation Entropy Based Instantaneous Energy Features

Localization of partial discharge (PD) is a reliable and necessary technique for early prediction of impending failure in transformer windings. Different PD sources have diverse impacts on insulation, and PDs measured at different locations exhibit unique energy characteristics. An approach based on the energy characteristics of the PD signal is proposed here to locate PD inside the winding. An electrical detection method has been considered with PD pulses measured at the top and bottom of the winding through high-frequency current transformers (HFCTs). To reveal multiscale intrinsic characteristics of the PD signal, authors extracted intrinsic mode function (IMF) using ensemble empirical mode decomposition (EEMD). Next, the instantaneous energy distribution-permutation entropy (PE-IED) values of the first several IMFs were calculated to capture the energy of the PD. Then, dimensionality reduction was ensured by the fast principal component analysis that uses the fixed-point algorithm method. Finally, adaptive density-based clustering using the nearest neighbor graph (ADBSCAN-NNG) approach is proposed to identify the local high-density energy components. The proposed method is verified using the winding of a vacuum cast coil transformer and the localization results reveal the feasibility of the proposed method in locating PDs over existing localization algorithms. The reduced complexity of the proposed algorithm ensures the accurate onsite PD localization in transformer windings.

Improving partial discharges location under dispersion and multi-path propagation

Dispersion and multi-path propagation distort partial discharge (PD) pulses that travel along power cables. Two new techniques to improve the location accuracy using double-ended methods are proposed: the inclusion of a pre-filter (PF), that increases the signal to noise ratio by properly selecting the filter bandwidth and the center frequency, and an echo suppression (ES) technique, that reduces the peak value of the interfering echoes.Two location algorithms (based on cross-correlation and the energy criterion function) are combined with the proposed techniques and their performances against noise, dispersion, and multi-path propagation are evaluated. Simulation results show that the proposed PF technique improves the location accuracy, even for high dispersion conditions, where the location error can be reduced down to half a meter. In addition, the proposed ES technique reduces the number of false detections in those cases where the first PD echo is close to the original PD. Experimental results confirm the benefits of the proposed PF technique, showing that, even under adverse conditions, the location error can be smaller than 1m in a 300m long cable.

Statistical Feature Extraction and System Identification Algorithms for Partial Discharge Signal Classification Using Laguerre Polynomial Expansion

In this paper, a novel algorithm for partial discharge (PD) pulse waveform analysis and separation based on orthogonal series expansion using Laguerre polynomials is proposed. A system identification technique is also developed to help increase the accuracy rate of the signal classification. Laguerre polynomial series expansions are applied on the PD pulses to effectively extract important information and well-discriminative features. Laguerre expansion coefficients inherently contain information about the amplitude (intensity) and the relaxation time of a dynamic system and this helps direct characterization of PD pulses. The proposed technique is applied to PD pulses acquired from a laboratory set of multiple PD sources. It is shown that this statistical method can classify partial discharge signals with high accuracies, even when the signals are visually indistinguishable. In this research, two methods are developed for system identification. One is based on a deterministic approach in the form of a recursive formula and the other one is a stochastic method based on a group Lasso methodology. The aim of system identification is to improve the classification accuracy by removing the effect of the measurement system from the observed PD waveforms. The accuracy rate of the proposed algorithm is evaluated using the signals that are captured from simultaneous artificial defects in high pressure SF <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</inf> and in low pressure air.

Partial Discharges in HVDC Insulation with Superimposed AC Harmonics

This paper describes the influence of AC harmonics in HVDC systems on one of the main indicators of high voltage insulation deterioration, partial discharge. HVDC systems may be subject to various disturbances including harmonics and ripples in the form of pulsating voltages. Experiments, performed on model void specimen at DC voltage with selected superimposed harmonics, are described. A novel approach for recording partial discharges at DC, based on synchronization with selected superimposed harmonics, is proposed. In this way individual partial discharge pulses, occurring during a given measurement time, are accumulated with respect to the phase angles of certain harmonics. The partial discharge inception threshold is analyzed for cases where the DC level is held constant and the AC magnitude and harmonic frequency are varied and for cases where the DC level is varied and the AC magnitude is held constant. In addition, a long term aging test where insulating paper and resin impregnated paper was subjected to pure DC voltage and DC containing 12th harmonic 600 Hz, is described. The aging test shows that the superimposed harmonic content resulted in an amplification of partial discharge presence. By mapping the discharge spots occurring on the void surface, it is possible to quantify the level of erosion in both cases. It was observed that in case of harmonic crest voltage being below partial discharge PD inception level the DC component is crucial, however when harmonics magnitude is above the inception voltage the role of AC is dominant relative to DC. Thus, the presence of harmonics may result in a strong acceleration of the deterioration of insulating materials subjected to DC voltage.

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.

Partial Discharge Analysis of SF6/N2 Gas Mixture Filled MV Circuit Breaker Using Time–Frequency Map Clustering Technique

Breakdown of gas insulated switchgears due to partial discharges (PD) is a major threat for the safe and reliable operation of power system network. In recent times, PD based diagnostics has been widely adopted by electrical utilities in order to evaluate the health of high voltage insulation system. However, very few reports are only available on the PD characteristics of gas insulated medium voltage (MV) circuit breaker prototypes, in particular with mixture of gases, over a wide range of pressure and voltage stress. Deep analysis of nature of PD signals in mixture of gas insulation and its time–frequency characteristics is important in order to develop efficient online condition monitoring systems for circuit breakers. This paper is aimed at the analysis of PD characteristics of a 22 kV MV circuit breaker prototype filled with SF6/N2 gas mixtures using time–frequency (T-F) mapping and clustering technique. PD tests are carried out at different gas pressures (1–5 bar) and at different gas mixture ratios. Important PD parameters such as PD inception voltage, phase resolved PD (PRPD) pattern and PD repetition rate are evaluated. Clustering analysis of PD signals from the time–frequency mapping is carried out and formation of three different clusters consisting of short, long and repetitive PD pulses is identified. Online real time monitoring of individual cluster density will be helpful to understand the degradation of gas insulation of circuit breakers. Keeping in mind the minimal usage of SF6 gas, from the results, it is noted that 10% SF6 gas can be mixed with 90% N2 at 3 bar pressure to get satisfactory PD performance of a 22 kV medium voltage circuit breaker.

Role of air bubbles in the breakdown of flowing transformer oil

In this paper, a liquid circulation system is constructed whereby gas bubble dynamics and electrical signal evolution preceding the oil gap breakdown are registered in stationary and flowing transformer oil. Based on a number of repetitive tests, two types of breakdown are classified: one takes place after bubble growth and originates in the bubble or the mixture of liquid and gas when the applied voltage is in the negative half-cycle; the other follows streamers resulting from the protrusion of bubble tip in the positive half-cycle. For each type of breakdown, partial discharge (PD) pulse is captured preceding the breakdown. As the oil flow rate varies, bubble dynamics change but the characteristics of each breakdown remain. In addition, it is found that oil breakdown voltage is improved as the flow rate increases. In terms of PD occurrence and bubble growth, the role of gas bubble in oil breakdown initiation, as well as the effect of oil flow on breakdown, is discussed.

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.

A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy.

To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD.

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.

Field verification of a novel and simple partial discharge detection method for generator applications

This study introduces a simple and novel partial discharge (PD) detection method for generator condition monitoring applications. The proposed method uses statistical operator ‘Variance’ which is proportional to the power of an electrical signal. It was validated by field measurement conducted at four in service hydro-generators: two having heavily aged windings and the other two having identical new. First, accuracy of detecting PD pulses was verified by comparing with wavelet-based method. The proposed method detected all the PD pulses with a ‘Precision’ higher than 90% and recall of 100%. As the second step phase resolved PD (PRPD) patterns generated using the proposed method were compared with commercial PD monitoring systems. It was found that the overall similarity was above 0.75. Finally, extracted PD pulses were used to calculate the polarity wise dissipated PD energies. The calculated energies were correlated with identified PD sources through series of off-line tests and visual inspections. It was observed that discharges due to loosen windings and corona activity were well correlated with estimated PD energies. All these validation processes indicated that the proposed method effectively and accurately detect PD pulses and can be used as a diagnostic tool for generator applications.

Influence of Insulating Material Properties on Partial Discharges at DC Voltage

Understanding a partial discharge mechanism at direct current (DC) is an actual research topic that requires both modeling, simulations and measurements. This paper describes an influence of insulating material properties on partial discharges at DC voltage. Modifications of the traditional model reflecting the conditions of partial discharges (PD) inception and post discharge processes at DC voltage have been proposed. The aim was to show the partial discharge mechanisms and draw attention to the role of parameters of insulation materials adjacent to the cavity at DC voltage. The investigations were performed on two kinds of dielectric material used in power cables. Various combinations of specimens were designed to reveal the effect of the material resistivity on the PD activity. Key observations referred to the impact of the void adjacent material resistance on the partial discharge inception voltage threshold at DC voltage. The modified PD model was applied to analyze both inception and post discharge recovery stage. The role of dielectric properties of material adjacent to the void was investigated, highlighting its impact during static inception stage and in charging stage. Despite many simplifications introduced in the model, measurement results have confirmed the role of the dielectric material surrounding the void on partial discharge dynamics. The average time interval between PD pulses revealed a systematic relationship with respect to the applied voltage and specimen resistivity. This value can be considered in the future research for diagnostic indicator at DC voltage.

VLF Dielectric Response and HF Localized Dielectric Discharge Measurement for Rotating Machine Insulation Assessment

This paper represents the very low frequency (VLF) dielectric response and high frequency (HF) localized dielectric discharge measurement for rotating machine insulation assessment. The integral conditions of stator winding insulation, the so-called aging condition, were investigated by the dielectric response measuring technique. The polarization and depolarization currents in the VLF range from 0.0001 to 1 Hz were measured under an external step electric field. The local conditions of stator winding insulation, the so-called weak spot condition, were examined by the partial discharge (PD) measuring technique. The PD pulse currents were detected by a capacitive sensor in a wide-band range, where the bandwidth of the measuring system was set between 30 kHz and 30 MHz. The medium voltage rotating machine rated 6.6 kV and 240 to 2,270 kW under different service conditions, i.e., new stator winding, moisture contamination, dirt contamination as well as thermally and severely aged insulation, was studied. Besides, four case studies on measuring the PD in service rotating machines rated 11–15 kV, 7100 to 211,000 kW as well as the attenuation of the PD pulses were conducted and demonstrated. With several case studies, this paper introduces new dielectric parameters, the so-called charge ratio (QR) and charge difference gradient (QDG), to identify the dielectric mechanism occurring in the stator winding insulation caused by the polarization and conduction processes. It can be concluded that the proposed parameters are a valuable tool for assessing the aging condition. In the case of the PD measurement, it was found that the PD pulse is strongly attenuated in the slot section. This paper also introduces the combination of both the dielectric response and PD measurement results, which can be very useful for assessing a complete insulation condition in the rotating machine. Moreover, the criteria for insulation conditions are suggested in this paper to evaluate the integral and local conditions for the rotating machine insulation. The local discharge in the machine causes electromagnetic waves, which may get released from the non-perfect characteristics of the enclosure. This signal may interfere with the functioning of other nearby electronic devices, especially the communication equipment. Therefore, maintaining the excellent condition of the insulation system by the proposed technique detailed in this paper will support the highly effective operational efficiency of the communication system as well.

Determining the exact location of partial discharge in coil insulation of high-voltage machine using high-frequency model of the machine

The current study is aimed at investigating the exact location of partial discharge (PD) in coil insulation of a 6 KV/250 KW induction machine. Since measured PD signals have frequency components of kilohertz (KHz) to megahertz (MHz), it is necessary to employ fast and very fast transient models of electrical machines for locating PDs. In this study, a PD current pulse with MHz frequency component is injected to the multi-conductor transmission line model of each turn of the coil with frequency validity of &gt;1 MHz. Considering coils as an interface between voltage source and finite element region, all circuit and field equations are rewritten to obtain the system final matrix, so that PD pulse is extracted. Experiment and simulation results show high accuracy of developed model to determine the exact location of PD.

Partial Discharge Pattern Recognition Based on 3D Graphs of Phase Resolved Pulse Sequence

Partial discharge (PD) is an important phenomenon that reflects the insulation condition of electrical equipment. In order to protect the safety of power grids, it is of significance to diagnose the type of insulation defects inside the equipment accurately and early through PD pattern recognition. In this article, phase resolved pulse sequence (PRPS) graphs in 3D were constructed by the PD pulse data of the gas-insulated switchgear (GIS) acquired, then the histogram of oriented gradient (HOG) features were extracted directly from the 3D PRPS graphs, and finally the attribute selective Naïve Bayes classifier was used to recognize the discharge pattern. In addition, this method was compared with two traditional methods, i.e., the statistical method and the grayscale gradient co-occurrence matrix method, from three aspects. The result shows that 3D PRPS graphs have different morphology characteristics in vision under different defects, and the similarity among different voltages applied is higher than among different defects, so it is reasonable to use them as the basis for PD pattern recognition. The contrast indicates that the HOG method not only has the highest accuracy with the least requirement for pretreatment and training, but it also has robustness when the voltage applied changes. Consequently, this method has the universality for PD pattern recognition that is based on 3D PRPS graphs.

Characteristics of AC PDs on epoxy insulation surface excited by different impulse voltages in SF6 gas

In this paper the behavior of partial discharges (PDs) on epoxy insulation under AC and impulse superimposed voltages are experimentally investigated in SF 6 gas. The lightning impulse (LI) and the switching impulse (SI) voltages with different polarity are superimposed on different phases of the AC voltage. PDs are measured by three sensors, a 50 Ω measurement impedance, an ultra-high frequency sensor and a photomultiplier tube. The max PD amplitude, PD pulse number and PD duration under AC voltage after an impulse voltage are summarized and analyzed. The effect of the impulse superposition phase, the impulse type and the impulse polarity on the characteristics of AC PDs are discussed. Results show that after a positive impulse voltage was applied on the positive semi-wave of AC voltage (0 to 180°), PDs in the negative half cycle of the AC voltage can be remarkably excited. A negative impulse voltage was applied on the negative semi-wave (180 to 360°), AC PDs can be initiated in the positive half cycle of AC voltage. PDs excited by a LI voltage is severe than excited by a SI. PDs excited by a positive impulse is intense than excited by a negative impulse.