Partial Discharge Activity Research Articles

Effect of Electrical Aging On PPLP Insulation Characteristics in LN2

The high-temperature superconductivity (HTS) DC cable has brought huge economic benefits and has been widely used in the system with its low line loss. The Polypropylene laminated paper (PPLP) tape is wrapped around the core due to its excellent insulation properties, which produces butt gaps. Since cables in HTS DC transmission system often withstand DC and AC composite voltage, these gaps make partial discharge (PD) easier to occur. Meanwhile, degradation occurs in PPLP under long-term electrical stress, which could threaten the safe operation of the power system. Therefore, this article conducted an electrical aging test in liquid nitrogen (LN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) to simulate the actual operating environment of PPLP, and also measured the conductivity, relative permittivity, PD, and breakdown strength of PPLP with different aging times in LN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . The results show that the conductivity basically increases with the increase of aging time and relative permittivity has a significant increase at the end of the aging stage. As the aging time increases, the PD activity of PPLP becomes more intense and the DC breakdown strength shows a downward trend. The reason for the deterioration of PPLP insulation performance after aging was analyzed. Besides, the effect of different alternating components on PD was studied in the case of constant effective voltage. When the alternating component decreases, the number of PD is significantly reduced.

Detection of Electrical Tree Formation in XLPE Insulation through Applying Disturbed DC Waveforms

Initiation and growth of electrical trees can be linked to the development of partial discharge (PD) activity within an insulation system. We present in this paper changes in PD characteristics that appear during the electrical tree growth in a crosslinked polyethylene (XLPE) based insulation at different stages of its degradation. A DC voltage with superimposed high frequency component is applied to the material specimens for the detection and characterization of PD traces observed during the degradation. The method is flexible and offers a possibility to vary DC voltage level independently of the superimposed disturbance, its frequency and rise time, including rapid voltage steps. It also enables to create a phase-locked phase-resolved PD pattern (PRPD) facilitating various analyses of the characteristics. It is postulated that measurements of the changes in PD characteristics found under such excitations can be used for performing diagnostics and prognostics of material condition. This approach also implies new possibilities to evaluate different material concepts.

Modelling of partial discharge pulse propagation in transformer windings using multi conductor transmission line modelling scheme

Power transformers are considered as an essential component in the electric power systems. They are placed in services in different circumstances like electrical, environmental and mechanical stresses, and through the progress of operation it can affect with various threats and hazards. Transformer failure should be diagnosed earlier in order to avoid its unsafe and unstable operation. The main reason for failure of insulation in power transformer is due to partial discharges. The partial discharge activity occurring in the transformer have to be identified earlier, afore it progresses into a complete discharge, otherwise it results in the maintenance of the device and causes loss of economic cost to the utilities. In this paper, multi conductor transmission line theory is used for the modelling of transformer windings and the partial discharge pulse propagation through the winding is simulated in the MATLAB Simulink. The effect of partial discharge on the windings are also discussed.

Characterization of Partial Discharge Activities in WBG Power Converters under Low-Pressure Condition

Many sectors, such as transportation systems, are undergoing rapid electrification due to the need for the mitigation of CO2 emissions. To ensure safe and reliable operation, the electrical equipment must be able to work under various environmental conditions. At high altitudes, the low pressure can adversely affect the health of insulating materials of electrical systems in electric aircraft. A well-known, primary aging mechanism in dielectrics is partial discharge (PD). This study targets internal PD evaluation in an insulated-gate bipolar transistor (IGBT) module under low-pressure conditions. The estimation of electric field distribution is conducted through 3D finite element analysis (FEA) using COMSOL Multiphysics®. The procedure of PD detection and transient modeling is performed in MATLAB for two pressure levels (atmospheric and half-atmospheric). The case study is the IGBT module with a void or two voids in the proximity of triple joints. The single-void case demonstrates that at half-atmospheric pressure, the intensity of discharges per voltage cycle increases by more than 40% compared to atmospheric pressure. The double-void case further shows that a void that is harmless at sea level can turn into an additional source of aging and couple with the other voids to escalate PD intensity by a factor of two or more.

Investigation of supraharmonics' influence on partial discharge activity using an internal cavity sample

This research paper investigated the influence of lower-order supraharmonics on partial discharge activity in an insulation system sample with a predefined spherical cavity defect. The supraharmonics were represented by repetitive sinusoidal oscillations, whose shape, amplitude, and natural frequency were based on typical trends measured or modeled in medium- to high-voltage electric subgrids containing power converters. The combination of a fundamental 50-Hz waveform and a one-per-period oscillation was applied to the test sample, and the occurring partial discharge activity was measured. Throughout the experiment, the oscillation's position and natural frequency were gradually changed. Afterward, partial discharge activity under identical conditions was simulated using an analytical model, and the phenomena observed during the experiment were explained by it. Changes accounting for the presence of supraharmonic oscillations were introduced to the model. Ultimately, conclusions about the significance of the changes in the partial discharge activity were drawn. Among those were predictions of increased aging rate, earlier inception of partial discharge activity, and suggestions for future works.

Partial Discharge Detection and Diagnosis of Transformer Bushing Based on UHF Method

Bushings are essential components in a power transformer where incipient partial discharge (PD) activities and its detection and diagnosis cannot be ignorant. In this paper, an oil-impregnated paper (OIP) bushing is modeled to investigate that the oil gaps between the flange and condenser body in a typical OIP bushing provide a feasible path to trace the electromagnetic signal. As a non-destructive method, UHF (Ultra High Frequency) is proposed to detect 4 typical inside and outside PD defects of 110 kV bushings. To analyze the parameters of different defects, not only phase-resolved partial discharge (PRPD) is presented, but also 16 detailed time-domain and frequency-domain parameters of UHF signal are involved. Then feature extraction and selection are conducted through comparative principal component analysis (PCA) and extremely randomized trees (ET) algorithms. It is revealed that the selected features are representative and ET greatly reduces the amount of data whilst ensuring high accuracy. Fault diagnosis for bushing is finally achieved via support vector machine (SVM) with the selected features. The presented work of bushing PD detection based on UHF sensors provides a complete solution for the bushing diagnosis in potential field application and maintenance.

Tailoring insulation surface conductivity for surface partial discharge mitigation

It is an urgent requirement to suppress surface partial discharge (PD) activities of dielectrics for modern electrical and electronic devices. Here, we introduce a method to mitigate the surface PD in a manner of tailoring insulation surface conductivity. A surface coating with a conductivity ranging from 10−13 to 10−10 S is developed to regulate the surface charge migration in the vicinity of the triple junction and to suppress effectively the surface PD activity. The excellent performance of this coating provides an approach for surface PD suppression at DC voltage, which has great application potential in the insulation design of DC electric power equipment, DC generators, power electronic devices, printed circuit board (PCB) layout, and various integrated electrostatic sensors and actuators.

Generative Adversarial Networks used for latent space Optimization: a comparative study for the Classification of Partial Discharge Sources

Hydrogenerators are strategic assets for power utilities. Their reliability and availability can lead to significant benefits. For decades, monitoring and diagnosis of hydrogenerators have been at the core of maintenance strategies. The main cause of hydrogenerator breakdown comes from failure of its high voltage stator, which is a major component of hydrogenerators. In our previous study, it was shown that more than 85% of stator failure mechanisms indicate the presence of Partial Discharges (PD) activity. PD are minute sparks that occur within voids inside high voltage insulation or in the air around the insulating system. Each PD event does not cause immediate failure, but it will slowly erode the insulation system and will lead to breakdown in years to decades. PD signal can be detected from the lead of the hydrogenerator while it is running, thus allowing for on-line diagnosis. Hydro-Québec has been collecting more than 33.000 unlabeled PD measurement files over the last decades including two types of measurement instruments. One of the instruments used is the Partial Discharge Analyzer (PDA). PDs have several different sources and each source is characterized by a specific signature. It is therefore essential to be able to automatically recognize the nature of the DPs in order to anticipate possible degradation. Expert rules to characterize these PD signals exist but these rules cannot be used as an automatic classification tool. Indeed, for certain ambiguous cases (conflict between classes) or the presence of several signatures at the same time (multi-classes), the expert's judgement remains essential.&#x0D; A Variationel AutoEncoder (VAE) is used for dimension reduction and projection into a 2D latent space to analyze the training data and then classify it. The 2D latent space of the VAE allows the data space to be restructured and reorganized to anticipate the performance of the classifier. The problem is how to optimize this latent space and thus obtain the best distribution of the different sources of PDs to maximize the chances of good responses from the classifier? To our knowledge, there is currently no method in the literature that clearly answers this question. What is certain, however, is that the optimization of the VAE learning is directly related to the quality of the learning base, i.e. a large size and a perfectly balanced database (all the cases are present and sufficiently represented). The objective of this paper is to compare the quality of the latent space obtained from the experts' rules with a latent space obtained directly from the input signal in an « End-to-End » approach.&#x0D; The first method concerns an original unsupervised deep learning method for PD recognition. Instead of labelling the PD measurement files by the expert for a supervised learning process, we use the rules developed by the experts of Hydro-Québec to create a feature vector from recognizable PD signatures. Indeed, labelling a sufficient quantity of signatures for a supervised approach is very time-consuming and therefore cannot be implemented. This is a common problem in the industry where more and more operational data is available but cannot be labelled by experts, who are busy with other tasks. Expert knowledge is then injected into a characteristic and feature vector. In the second method, several Generative Adversarial Networks (GANs) associated with several types of PDs are thus used to generate artificial signals in order to increase the size of the learning base and especially to balance it. A new latent space is thus obtained from the learning of the VAE exclusively carried out on data generated by the GANs.&#x0D; Validation tests based on the 33,000 measurements with metrics for evaluating the performances of the various latent spaces are used.

Review of Edge-based Image Segmentation on Electrical Tree Classification in Cross-linked Polyethylene (XLPE) Insulation

Electrical trees are the degradation events most linked with partial discharge (PD) activity in cross-linked polyethylene (XLPE) insulation of high voltage (HV) cables. To investigate tree structures and forms, study of electrical tree structures for morphological analysis often carried out using optical microscopy. However, since the noise induced by the occlusion and illumination, as well as by non-uniform intensity either from optical device’s setting or the non-standard readout procedure, causes the deterioration of the original microscopy images, resulting in critically loses of information pertaining the tree structures making it difficult to obtained accurate measurement. Image segmentation is one of the potential solutions as it is well-suited for extracting information or certain features from microscopy images. This paper provides review of several segmentation techniques applied on the classification of electrical tree image acquired through lab environment. The works can be separated into three stages. The first step includes the preparation of samples and collection of treeing images by means of real-time microscopy observations. The captured data would then be pre-processed to achieve image binarization. In the next step, image segmentation process is conducted using existing edge-based segmentation methods including Prewitt, Roberts, Canny and Kirsch’s templates. Later, comparative analysis will be performed using IQA (image quality assessment) metrics of accuracy, sensitivity, specificity, false-positive-rate and the Matthews Correlation Coefficient (MCC) as the final step. Performance-wise indicates that Kirsch’s template able to segment most of the treeing pixels with accuracy of 97.63%, 63% sensitivity, 98.18% specificity and 0.46 MCC while showing low pixels misclassification. This result provides better justification for the integration of the edge-based technique in developing image segmentation algorithm well-matched for the electrical tree analysis in the future.

Partial Discharge Activity Diagnosis in Electrical Cable Terminations Using Neural Networks

The electrical cable is an essential equipment in electricity transportation and distribution which are generally carried out using its types consisting in overhead lines or underground cables. The terminations are among the primordial accessories in electrical cable, they are intended to cover and protect the ends presenting the weak point of failure of an electrical cable because in these zones the electrical stress increases so much. Consequently, partial discharge activity will eventually occur and develop into arcing (Elaggoune et al. in Deuxieme Conference Internationale Sur La Maintenance, La Gestion, La Logistique Et L’electrotechnique, Enset D’oran, Algeria pp 1–5, 2012; Elaggoune in Localisation des sources potentielles des decharges parasites creees, par un dispositif experimental de detection des Decharges Partielles dans un câble de l’energie electrique.memoire de magister, Universite de Laghouat, Algerie, 2012; Goyal in Ind Eng Lett 4:15–20, 2014). Therefore, the terminations must be diagnosed periodically in order to avoid an insulation system breakdown, which causes a power outage and consequently excessive economic losses. The objective of this work is to assess the health of electrical cable terminations usingthedissolved gas analysis which is an effective chemical method toguarantee a detection of partial discharges in cable terminations (Samsudin et al. in Incipient fault detection in 33/11kV power transformers by using combined dissolved gas analysis technique and acoustic partial discharge measurement and validated through untanking, Modern Electric Power Systems (MEPS), Wroclaw, Poland, pp 1–6, 2010; Faisal et al. in Int J Electr Eng Inform 4: 186–201, 2012; Kumpulainen in Aspects and directions of internal arc protection, Doctoral Thesis, University Of Vaasa, Finland 2016; Vahamaki in Arc protection as integrated part of line protection relays, See http://www.cigaproject.ch/wp/docs/Arc%20protection%20integrated%20in%20protection%20relays.pdf , 2020). However, this method failed to identify this phenomenon in some cases. To remedy with this problem, Radial Basic Functions neural network is applied as simulation tool. Therefore, the Radial Basic Functions neural network, based on dissolved gas analysis, can be used successfully in monitoring electrical cable terminations against partial discharges phenomenon.

Application of the Gaussian Mixture Model to Classify Stages of Electrical Tree Growth in Epoxy Resin.

In high-voltage (HV) insulation, electrical trees are an important degradation phenomenon strongly linked to partial discharge (PD) activity. Their initiation and development have attracted the attention of the research community and better understanding and characterization of the phenomenon are needed. They are very damaging and develop through the insulation material forming a discharge conduction path. Therefore, it is important to adequately measure and characterize tree growth before it can lead to complete failure of the system. In this paper, the Gaussian mixture model (GMM) has been applied to cluster and classify the different growth stages of electrical trees in epoxy resin insulation. First, tree growth experiments were conducted, and PD data captured from the initial to breakdown stage of the tree growth in epoxy resin insulation. Second, the GMM was applied to categorize the different electrical tree stages into clusters. The results show that PD dynamics vary with different stress voltages and tree growth stages. The electrical tree patterns with shorter breakdown times had identical clusters throughout the degradation stages. The breakdown time can be a key factor in determining the degradation levels of PD patterns emanating from trees in epoxy resin. This is important in order to determine the severity of electrical treeing degradation, and, therefore, to perform efficient asset management. The novelty of the work presented in this paper is that for the first time the GMM has been applied for electrical tree growth classification and the optimal values for the hyperparameters, i.e., the number of clusters and the appropriate covariance structure, have been determined for the different electrical tree clusters.

Entropy-Based Characterization of the Transient Phenomena—Systemic Approach

The difficulties of predictive maintenance of power grids are related to the large spread of electrical infrastructures and the definition of early warning indicators. Such indicator is the partial discharge activities—which can be very informative about the rising insulation problems of electrical materials. However, the detection and the localization of the partial discharges are very complicate tasks and are currently subject to intensive studies in both theoretical and practical domains. The traditional way to approach the global surveillance of partial discharge sources is to first detect it and the second is to attempt to localize their positions. Despite the numerous proposed approaches, based on advanced transient signal processing tools, there is no any operational technique to efficiently asses the partial discharge sources in a real power network. In this context, our paper proposes a new approach based on the global evaluation of entropy of transient phenomena detected in a power network, without needing any localization of the sources of these phenomena. We will show that this approach provides an effective evaluation of partial discharges sources. Moreover, since the technique requires a reduced number of sensors, it is very advantageous to use in real contexts.

Investigation of Partial Discharge Due to Particle Movement in Power Transformer Using Computational Fluid Dynamics and Monte Carlo Technique

The paper deals with the movement of metallic particle contaminants in transformer oil and partial discharges due to its presence inside the power transformer. Transformer oil has several particle contaminations, which float and travel along with oil in the gap between transformer discs and inner pressboard cylinder. Activity of partial discharge (PD) is analyzed using Monte Carlo technique and Computational Fluid Dynamics. Probability of PD is calculated using Monte Carlo simulation, and particle movement is traced using Computational Fluid Dynamics. Using the above methods, point of contact between particle and transformer disc under different conditions is determined. Electric stress on the particle due to disc voltage is calculated theoretically. In the present simulation, impact of field multiplication factor is considered for initiation of PD. From the results, it is observed that particle movement and partial discharge are influenced by field multiplication factor, oil velocity, type of material and starting point of the particle.

A Study on the Life Estimation and Cavity Surface Degradation Due to Partial Discharges in Spherical Cavities within Solid Polymeric Dielectrics Using a Simulation Based Approach.

Partial Discharges (PD) in cavities are responsible for the greatest ageing rate in polymeric solid dielectrics due to chemical and physical deterioration mechanisms activated by the charge carriers, Ultra Violet (UV) radiation and local temperature rising during PDs activity. From the above, it is necessary to develop prognosis tools based on PDs measurements as diagnostic quantities in order to infer the time-to-breakdown, life, of solid dielectrics for improving the reliability of electrical assets, especially in current applications where they are subject to great electrical stresses in voltage frequency and magnitude. In this paper, the degradation in polymeric materials induced by PDs in cavities is briefly discussed from a phenomenological point of view, and then it is quantitatively evaluated using a simulation-based approach and a new proposed damage function. The time-to-breakdown calculated from simulations exhibits good agreement when compared with experimental measurements. Additionally, an analysis on the effect of the magnitude and frequency of the applied voltage on the degradation rate is also presented and the effectiveness of a degradation indicator, proposed by other authors, is evaluated under different stress conditions.

Analysis of Cavity PD Characteristics’ Sensitivity to Changes in the Supply Voltage Frequency

The supply voltage frequency effect on partial discharge (PD) phenomena has continued to draw research interest. Although most high voltage equipment operates at power frequency (50/60 Hz), testing is often done at different frequencies for various reasons. Despite some agreements and inconsistencies for the research findings of PD activity’s frequency dependence, there has been consensus on the recognition of the discharge mechanism parameters that influence how the supply voltage frequency affects PD activity. These parameters include statistical time lag, discharge area surface conductivity, and the residual charge decay. In this paper, a 3-capacitor model (ABC) is used to simulate how the changes in the discharge mechanism parameters influence PD characteristics as a function of the supply voltage frequency. The findings are that the phase-resolved partial discharge pattern (PRPDP) and PD repetition rate (PDRR) characteristics are more sensitive to variations in the probability of the seed electron availability at higher frequencies of the supply voltage. The opposite trend is observed for the cavity surface resistance. At lower resistance of cavity surface, the PRPDP and PDRR characteristics are more sensitive to changes in the supply voltage frequency than at higher resistances. The paper also confirms that incorporating equivalent resistances in the ABC model makes it more authentic than the model comprising of capacitors only.

Review of Partial Discharge Activity Considering Very-Low Frequency and Damped Applied Voltage

When detecting the presence of partial discharge (PD) activity in the insulation system in high-voltage equipment, the excitation voltages at variable frequency have been widely used instead of power-frequency (50/60 Hz) sinusoidal voltage in order to reduce the charging power. This work reviews the relevant research on PD activity at very low frequency (VLF) method, including sinusoidal or cosine-rectangular voltage shape, and damped AC (DAC) method. Based on the research history and development status, some major PD characteristics, such as PD inception voltage (PDIV), PD amplitude, PD charge, PD phase-resolved pattern, and several hot issues, such as surface charge decay and statistical time lag, have been discussed. Moreover, the advantages, disadvantages, and applied conditions of two reviewed methods has been summarized. Finally, the prospects have been made on the main development trends of this research field in the future.

Designing a HVDC Insulation System to Endure Electrical and Thermal Stresses Under Operation. Part I: Partial Discharge Magnitude and Repetition Rate During Transients and in DC Steady State

This paper has the purpose to investigate HVDC insulation design considering real operating conditions, when DC steady-state is affected by frequent voltage transients or load variations that may be present during all life. Electrical field distribution in insulation, and in insulation defects, may change significantly from DC steady-state when voltage and load vary with time, which can cause partial discharge activity often not been properly accounted for at the design stage. The Part I of this paper is dedicated to prove, through experiments, models and simulations, that electrical and thermal transients may incept partial discharges in defective insulations during cable energization, voltage polarity inversion at a constant nominal load, as well as during load variations at a constant nominal voltage. This can cause accelerated aging and premature breakdown even if the insulation system is designed properly to withstand DC electrothermal stress, without partial discharges in steady state, for all life, as it will be shown in Part II. Focus is on cables, but the approach described here is general for any DC insulation system.

A UHF Sensor Based Partial Discharge Monitoring System for Air Insulated Electrical Substations

A Partial Discharge (PD) monitoring system for Air Insulated Substations (AIS) has been developed for monitoring of partial discharge activities of various insulators and insulating media that are distributed over an electrical substation. The proposed system utilizes four UHF sensors, which are to be placed around the area of interest so that any impulsive UHF signal originated from PD activity can be captured and subsequently processed for detection and localization of multiple PD sources. To improve the localization accuracy and to improve the detection specificity, i.e. to eliminate false impulsive UHF signals originated from other sources, a novel nearest-neighbour based approach is proposed, which utilizes the physical layout of the AIS to identify most probable PD source locations. The proposed algorithm can accurately find the PD source locations even in the presence of high time-delay-of-arrival (TDOA) estimation error by making necessary corrections in the PD source location estimation. The proposed algorithm has been field trialed in many small and medium-size distribution substations. A detailed comparative study with the other algorithms in different extreme situations has been performed with the help of simulation and laboratory-based testing. The obtained results are worthwhile for supporting widespread implementation of the proposed method.

Insulation Failure Quantification Based on the Energy of Digital Images Using Low-Cost Imaging Sensors

Insulation faults in high-voltage applications often generate partial discharges (PDs) accompanied by corona activity, optical radiation mainly in the ultraviolet (UV) and visible bands. Recent developments in low-cost, small-size, and high-resolution visible imaging sensors, which are also partially sensitive to the UV spectral region, are gaining attention due to their many industrial applications. This paper proposes a method for early PD detection by using digital imaging sensors, which allows the severity of insulation faults to be assessed. The electrical power dissipated by the PDs is correlated to the energy of the acquired visible images, and thus, the severity of insulation faults is determined from the energy of the corona effect. A criterion to quantify the severity of insulation faults based on the energy of the corona images is proposed. To this end, the point-to-plane gap configuration is analyzed in a low-pressure chamber, where digital image photographs of the PDs are taken and evaluated under different pressure conditions ranging from 10 to 100 kPa, which cover the typical pressure range of aeronautic applications. The use of digital imaging sensors also allows an early detection, location and quantification of the PD activity, and thus assessing the severity of insulation faults to perform predictive maintenance tasks, while enabling the cost and complexity of the instrumentation to be reduced. Although the approach proposed in this paper has been applied to detect PDs in aeronautic applications, it can be applied to many other high-voltage applications susceptible of PD occurrence.

Enhanced partial discharge location determination for transformer insulating oils considering allocations and uncertainties of acoustic measurements

The consequence of partial discharge (PD) activities inside transformer may result in a catastrophic failure. The exact PD location is based on precisely computation of the time difference of arrival (TDOA) between the signals at different acoustic sensors, therefore, the generalized cross-correlation approach is used to accurately determine TDOA between the sensor signals. Reducing the sensor location errors is necessary to identify the exact PD source location. 87 PD fault locations with 13 suggested sensor’s locations are presented to determine the best location of the sensors. The best sensor’s locations are determined based on the behavior of the maximum and minimum errors for each sensor’s locations. A proposed ANN model is constructed with different uncertainties of TDOA measurements and estimations. The ANN model is constructed based on 15,877 PD locations for each 0%, 5%, 10%, 15%, and 20% uncertainty noise of TDOA based on the optimal sensor’s location. Experimental works are carried out to verify the robustness of the ANN model. The maximum error of ANN model to determine the exact PD location is 2.74 cm with 20% uncertainty noise. On the other hand, the maximum error of other literature models is about 7 cm with the same uncertainty noises.