High Voltage Insulation Systems Research Articles

Effect of selected parameters on the electrical strength of a high-voltage vacuum insulation system

Effect of selected parameters on the electrical strength of a high-voltage vacuum insulation system

Investigation of Novel Solid Dielectric Material for Transformer Windings.

Improvement techniques aimed at enhancing the dielectric strength and minimizing the dielectric loss of insulation materials have piqued the interest of many researchers. It is worth noting that the electrical breakdown traits of insulation material are determined by their electrochemical and mechanical performance. Possible good mechanical, electrical, and chemical properties of new materials are considered during the generation process. Thermoplastic polyurethane (TPU) is often used as a high-voltage insulator due to its favorable mechanical properties, high insulation resistance, lightweight qualities, recovery, large actuation strain, and cost-effectiveness. The elastomer structure of thermoplastic polyurethane (TPU) enables its application in a broad range of high-voltage (HV) insulation systems. This study aims to evaluate the feasibility of using TPU on transformer windings as a solid insulator instead of pressboards. The investigation conducted through experiments sheds light on the potential of TPU in expanding the range of insulating materials for HV transformers. Transformers play a crucial role in HV systems, hence the selection of suitable materials like cellulose and polyurethane is of utmost importance. This study involved the preparation of an experimental setup in the laboratory. Breakdown tests were conducted by generating a non-uniform electric field using a needle-plane electrode configuration in a test chamber filled with mineral oil. Various voltages ranging from 14.4 kV to 25.2 kV were applied to induce electric field stress with a step rise of 3.6 kV. The partial discharges and peak numbers were measured based on the predetermined threshold values. The study investigated and compared the behaviors of two solid insulating materials under differing non-electric field stress conditions. Harmonic component analysis was utilized to observe the differences between the two materials. Notably, at 21.6 kV and 25.2 kV, polyurethane demonstrated superior performance compared to pressboard with regards to the threshold value of leakage current.

Application of a Phase Resolved Partial Discharge Pattern Analysis for Acoustic Emission Method in High Voltage Insulation Systems Diagnostics

An alternative method for analysis of acoustic emission (AE) signals generated by partial discharges (PD), based on a correlation between voltage phase run and AE pulses, so called phase resolved PD pattern (PRPD), is presented in the paper. PRPD pattern is a well-known analysis tool commonly used in such PD diagnostic methods as conventional electrical and UHF ones. Moreover, it yields various signal analysis abilities and allows a direct correlation indication between measurement results achieved using different methods. An original PRPD measurement methodology applied for AE method as well as some exemplary measurement results and further data analysis capabilities are presented in the paper. Also a comparative analysis of PRPD patterns achieved using various measurement methods and different PD source configurations have been investigated. All presented experiments were done under laboratory conditions using PD model sources immersed in the insulation oil. The main purpose of the presented research is to indicate an all-embracing analytical tool that yields an ability to direct comparison (qualitative as well as quantitative) of the AE measurement results with other commonly applied PD measurement methods. The presented results give a solid fundamental for further research work concerning a direct correlation method for AE and other described in the paper diagnostic techniques, mainly in order to continue PD phenomena analysis and assessment in real life high voltage apparatus insulation systems under normal onsite operation conditions.

Quantitative Analysis of Surface Partial Discharges through Radio Frequency and Ultraviolet Signal Measurements

In high voltage insulation systems, dielectric materials may be exposed to partial discharges (PD), which can lead to equipment failures and safety hazards. Therefore, it is crucial to detect and characterize PD activity on the surface of insulation systems. Techniques such as radio frequency signal analysis and ultraviolet radiation emission detection are commonly used for this purpose. In this research study, an analysis was conducted on the signals emitted by surface PD in the radio frequency and ultraviolet radiation emission ranges. The goal was to indicate possible directions for further basic research aimed at building a knowledge base and improving measurement methods. The analysis confirmed that radio frequency and ultraviolet signal analysis can provide important information about the activity and location of PD on the surface, including the intensity and nature of PD. The experimental investigation presented in this paper provides valuable insights into the potential for using radio frequency and ultraviolet signals to enhance diagnostic techniques for monitoring the condition of insulation systems in high-voltage equipment.

Localization of Partial Discharge Using All-Fiber System Based on Crystal Fluorescent Fiber

Partial discharge (PD) is a common defect to determine the quality of high-voltage power equipment insulation systems. Localization of PD is very important for equipment maintenance and risk assessment. A localization algorithm of received signal strength (RSS) using demodulating PD optical signal is proposed. This is based on the optical effects caused by PD, which influence the optical signal that is transmitted through an optical fiber sensor placed inside the equipment. Compared with plastic fluorescent fiber, SiO2 cladding YAP:Ce crystal core fluorescent fiber (SYCF) with high luminous efficiency, low loss, and strong anti-electromagnetic interference capability is selected as the sensing unit to detect the received optical signal strength of PD, through the all-fiber optic sensing system to achieve the PD location in 3-D space. Based on the optical RSS localization method, the range of localization deviation of PD under 37-kV applied voltage is measured and studied. The average localization deviation and the error are 6.9 cm and 7.4%, respectively. Comparing the optical signal location method based on RSS with other methods of PD localization, the advantages and accuracy of the optical location method proposed in this article are outstanding. Therefore, the all-fiber system with SYCF via optical signal has a wide application prospect in the spatial localization of PD.

Use of Data-Driven Approaches for Defect Classification in Stator Winding Insulation

Partial discharges (PD) in the high voltage insulation systems are both a symptom and cause of terminal and impending failures. The use of data-driven methods based on PD measurements will enable predictive strategies to replace traditional maintenance strategies. This paper employs machine learningbased classification models to identify and characterizePD signals originating from lab-made artificial defects in epoxy-mica material samples. Three different PD sources were studied: surface discharges in air, corona discharges, and discharges caused by internal cavities/delaminations. To generate high-quality datasets for the training, validation, and testing of classification models, Phase-Resolved PD (PRPD) data for each test object was obtained at room temperature under 50 Hz AC excitation at 10 % above the PD inception voltage (PDIV) of each sample. Relevant statistical and deterministic features were extracted for each observation and were labeled based on the defect type (supervised learning). Finally, the trained and validated ML models were used to identify PD sources in the service-aged stator winding insulation. Support vector machines (SVM), ensemble, and k-nearest neighbor (kNN) algorithms achieved significantly high accuracy (≥ 95 %) of defect identification.

Recognition of Fused Partial Discharge Patterns in High Voltage Insulation Systems: A Hybrid DCNN and SVM Based Approach

The performance assessment of insulation materials within the components of a high voltage power system depends upon effective condition monitoring techniques of the insulation. One of the important phenomena to be considered in the diagnostics and performance assessment of high voltage insulation materials is the Partial Discharge (PD) measurement. The improved Deep learning techniques help in the effective identification and classification of various sources of PD mechanism. In this work, Deep Convolution Neural Network (DCNN) technique is proposed for the fusion of several features that are extracted from the PD signals. Three-dimensional Phase Resolved patterns of PD images can be used for training the DNN. For the classification of PD patterns, linear and non-linear type of multi-class support vector machine has been proposed. The results obtained are compared with linear SVM and non-linear SVMs with the polynomial kernel, Radial Basis Function (RBF) kernel, and Sigmoidal kernel. The SVM type with a higher pattern recognition rate is identified to be effective in PD pattern recognition and classification. The results of the proposed work show that the fusion approach of PD patterns supports applying huge PD data sets as input, generated by multiple faults, for effective PD pattern recognition.

Classification of Partial Discharges Originating From Multilevel PWM Using Machine Learning

A partial discharge (PD) is a small, localized breakdown which often appears in high-voltage insulation systems. PDs can be initiated within material defects such as voids or cracks when exposed to sufficiently high electric field levels. In this research work, we study and propose a machine learning (ML) approach to detect and classify PDs originating from multilevel pulsewidth modulation (PWM) waveforms as utilized in power electronic devices, such as inverters and variable frequency drives. Due to the increased use of power conversion units, new types of higher magnitude PDs have been observed to increase insulation degradation. By creating a cavity placed at different locations within the test object and exposing it to PWM voltage waveforms with two different rise times, a total of 345 660 PD events are recorded and organized into ten different classes. The maximum PD amplitude, duration, the time distance between consecutive PDs, and the area under the PD are used as features for PD classification. A unique way of concatenating a sequence of the extracted features to capture the temporal dependence of consecutive PDs is also presented. It is observed that when creating a sequence of information from consecutive observed PDs, a significant increase in classification accuracy can be obtained. Trained classifiers based on ensemble bagged decision trees (DTs) and long short-term memory (LSTM) architecture resulted in 95.3% and 98.5% average classification accuracy on test data respectively.

In-depth exploration of partial discharge modelling methods within insulations

Partial discharge (PD) activities are the essential contributor of insulation failure, which constitute more than 60% for damaging high voltage equipment. This article presents a critical review on the modelling of partial discharge using different techniques. Monitoring partial discharge activities is essential to enhance the comprehension on the phenomena of partial discharge through modelling process. A lot of tools have been introduced to assist PD measurement in diagnosing and monitoring the condition of high voltage insulation system. This article will evaluate the development of important PD models such as three-capacitance model, Pedersen's model, Finite Element model (FEA), and Niemeyer's model. This review attested that the research studies done on partial discharge are still deficient and there are still countless of study on partial discharge to be carried out. The parameters that have influence on partial discharge occurrence inside cavities in solid dielectric materials are statistical time lag, inception field, effective charge decay time constant, and cavity surface time constant. The initial free electron required for the existence of a PD is provided by the electron surface emission and volume ionization with the cavity while applied voltage's amplitude and frequency are the stress conditions affecting PD behavior in cavities as well as cavity's size, shape, and location. A Comparison regarding modeling time and accuracy of the model between different models of partial discharge have been done in this article. From where it can be concluded that the FEM and Niemeyer's model are suitable for PD due to their average cost and low degree of application, but FEM is commonly used because it is more accurate and easiest model. Moreover, from a comparison of advantages and limitations of different models, it is shown that FEM model has one major limitation that the large amount of data is required during meshing process. Besides, Niemeyer's model is not accurate due to the fundamental assumptions made and these assumptions are not clearly justified with high reliance over immeasurable free parameters. • Examined distinct PD modelling techniques for better insight of PD dynamics. • Several PD models are developed on basis of Maxwellian and circuital approach. • Comparison between Existing PD Model on the basis of various parameters. • Comparison of modeling time and accuracy between various PD models is done. • Advantages and Limitations of various PD models are discussed and analyzed.

Surface modification of silicone rubber by CF4 radio frequency capacitively coupled plasma for improvement of flashover

The flashover performance of insulating materials plays an important role in the development of high-voltage insulation systems. In this paper, silicone rubber (SIR) is modified by CF4 radio frequency capacitively coupled plasma (CCP) for the improvement of surface insulation performance. The discharge mode and active particles of CCP are diagnosed by the digital single-lens reflex and the spectrometer. Scanning electron microscopy and x-ray photoelectron spectroscopy are used for the surface physicochemical properties of samples, while the surface charge dissipation, charge accumulation measurement, and flashover test are applied for the surface electrical characteristics. Experimental results show that the fluorocarbon groups can be grafted and the surface roughness increases after plasma treatment. Besides, the surface charge dissipation is decelerated and the positive charge accumulation is inhibited obviously for the treated samples. Furthermore, the surface flashover voltage can be increased by 26.67% after 10 min of treatment. It is considered that strong electron affinity of C–F and increased surface roughness can contribute to deepening surface traps, which not only inhibits the development of secondary electron emission avalanche but also alleviates the surface charge accumulation and finally improves the surface flashover voltage of SIR.

Influence of additional dielectric coatings on the electrode surfaces on selected parameters of acoustic emission signals in high-voltage gas insulation systems

The paper is devoted to the study of impact of additional dielectric coatings placed on the surface of the electrodes on the operation of a high-voltage insulation system with air as insulating medium. The study mainly focused on measurement of partial discharges (PDs) with the use of acoustic emission method. Tests were conducted in various levels of chamber pressure. Applicable parameters of recorded signals were analyzed with the use of applications developed in the environment of MATLAB software. The results of analyses clearly show that presence of additional coatings has impact on signal parameters and thus indirectly on PDs. The use of coatings reduces the duration of a single pulse (event) and increases its amplitude. Furthermore, the influence of degradation of the coating caused by individual discharges on the values of the analyzed parameters was analyzed. The study has shown, however, that this effect is negligible.

Measurement and Numerical Simulation of Discharge Characteristics in Air at Medium Frequency Voltages

Due to the increasing use of power electronic converters in electrical grids, high voltage insulation systems are stressed by medium frequency voltages. The breakdown voltage of air decreases with increasing frequency especially for inhomogeneous electric fields. Here, breakdown voltages for different homogeneous and inhomogeneous electric fields are measured. Finite element method plasma simulations of space charge generation and their influence on the electric field distribution before the breakdown are done to support the explanation of the decreasing breakdown voltage in the frequency range from 50 Hz up to 30 kHz.

Nanoparticles Application in High Voltage Insulation Systems

Insulation systems of electrical apparatus (fluid and solid) include transformer oil, insulating paper, glass, ceramic, mica and rubber. Normally, they are designed well to be stable at high temperatures and provide excellent electrical insulating properties in different operating loads. There is an increasing demand to modify the effectiveness and capabilities of these insulation systems to overcome various issues (e.g., overheating, overloading and short circuits). Moreover, they should match the new high voltage insulation requirements, safety, ultra-high voltage, and being environmentally friendly are some of them. Nanoparticles (NPs) are promising compensator materials that can be used to develop insulation systems with good thermophysical and dielectric properties. The results of many studies show marvelous enhancements in electrical and thermal properties. In this paper, the authors present a review that focuses on different NPs that are used in several aspects of insulation systems to improve the dielectric as well as the heat transfer properties of high voltage insulation applications. The review covers NPs types, the preparation methods, analysis approaches, different properties, performances and results, challenges, and the limitation of the use of NPs.

Review of interface tailoring techniques and applications to improve insulation performance

Interfaces between different materials, for instance, electrode–dielectrics and vacuum/air/SF6-insulators and oilpaper, are universal in high-voltage insulation systems. Targeted tailoring of the interfacial properties, e.g. chemical structures, micro-/nanoscale morphology, the charge injection barrier, trap states, and surface conductivity, is thought to be an effective approach to improve insulation properties such as breakdown and flashover strength, corona/tracking resistance, and wettability. In this article, the authors review recent progress in interface tailoring methods and their application to improve various insulation properties. The potential application scenarios of interface tailoring in different facilities are first summarised, and the desired insulation properties of various applications are highlighted. Interface tailoring methods are classified as physical/chemical surface modification and surface coating (inorganic, organic and composite), and the features of different techniques are described in detail. The structure–property relationships between interfacial states and various microscopic parameters such as charge injection barrier, trap distribution and surface conductivity are discussed together with the tailoring mechanisms for different insulation properties. Finally, the future development prospects of interface tailoring methods and their applications, e.g. multifunctional coating and stimuli–response smart coating, are presented.

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.

Classification of Partial Discharge Images Using Deep Convolutional Neural Networks

Artificial intelligence-based solutions and applications have great potential in various fields of electrical power engineering. The problem of the electrical reliability of power equipment directly refers to the immunity of high-voltage (HV) insulation systems to operating stresses, overvoltages and other stresses—in particular, those involving strong electric fields. Therefore, tracing material degradation processes in insulation systems requires dedicated diagnostics; one of the most reliable quality indicators of high-voltage insulation systems is partial discharge (PD) measurement. In this paper, an example of the application of a neural network to partial discharge images is presented, which is based on the convolutional neural network (CNN) architecture, and used to recognize the stages of the aging of high-voltage electrical insulation based on PD images. Partial discharge images refer to phase-resolved patterns revealing various discharge stages and forms. The test specimens were aged under high electric stress, and the measurement results were saved continuously within a predefined time period. The four distinguishable classes of the electrical insulation degradation process were defined, mimicking the changes that occurred within the electrical insulation in the specimens (i.e., start, middle, end and noise/disturbance), with the goal of properly recognizing these stages in the untrained image samples. The results reflect the exemplary performance of the CNN and its resilience to manipulations of the network architecture and values of the hyperparameters. Convolutional neural networks seem to be a promising component of future autonomous PD expert systems.

Hyperspectral imaging of high voltage insulating materials subjected to partial discharges

Abstract Partial discharges are complex and dynamic phenomenon appearing in high voltage insulation systems. They are characterized by physical and chemical effects that occur in the microstructure of insulating materials which lead to their deterioration or breakdown. Much research and scientific effort is devoted to the understanding of the underlying physical mechanisms of partial discharge and the development of diagnostic methods. Long-term partial discharge activity results in micro- and macroscopic changes in the structure of the system, leading to the insulation breakdown. The goal of this paper is to highlight the hyperspectral analysis as a novel assessment techniques of dielectric surface aging, especially of materials used for high voltage insulation. The experiments have been performed on insulation specimens containing a tiny void subjected to high electric field causing partial discharges. In order to test the performance of the method, various classes of dielectric materials were investigated. The presented methodology revealed the effects of insulation erosion and deterioration. It was demonstrated that hyperspectral imaging can localize the aged clusters in different wavelengths spectral regions. The promising advantage of this methodology is a potential for diagnostic applications of power equipment in the field.

Electric field stress determination tool for MV insulation system models

AbstractThe evolution of teaching methods concerned with electrical engineering enables the development of new measurement stands or the improvement of existing educational methods. The advancement in engineering equipment is affecting the formation of new methodologies in teaching. Nevertheless, new educational techniques should be implemented by taking into account understanding of a physical basis of studied phenomena. This goal may be achieved via a combination of practical exercises conducted on testing stands, during evaluation, by means of modern computerized tools. In this paper, we described the combination of a numeric study of electric field (EF) distribution and an improved analog method analysis performed on an educational set‐up. For an effective understanding of technical problems of high‐voltage (HV) and medium‐voltage technology, the presented educational tool combines an experimental method of EF equipotential line mapping and EF strength determination with an improved analog method of EF strength inside the HV insulation system, which is determined by means of differential equation calculation implemented on the developed software. For fast verification of the results, computer simulations were applied. It allows for presenting the strong and weak points of both used methods. Analyzed models reproducing real devices were built on different scales; thus, achieved results reflect the real values and may be used for study help during seminars.

Improved space charge transport model in bi‐layer dielectrics—considering carrier dynamic equilibrium

The Maxwell–Wagner model and bipolar charge transport model both aim at describing the charge transport behaviours in dielectrics. The Maxwell–Wagner model performs well in calculating the polarisation current and electric field distribution, but it is too macroscopic to describe the microscopic behaviours of space charges. The bipolar charge transport model can well simulate the space charge accumulation, but it cannot quantitatively relate the microscopic transport behaviours with the macroscopic conductivities of dielectrics. Considering the shortages of the above two models, an improved charge transport model, named carrier dynamic equilibrium model, was proposed in this study to simulate the polarisation process of the low-density polyethylene/ethylene propylene diene monomer bi-layer dielectric by introducing a source term of carrier dynamic equilibrium. Effects of carrier mobility and non-equilibrium carrier lifetime on the simulation results were explored, and a comparison among the results of measurement and different models was also made. Compared with the Maxwell–Wagner model and bipolar charge transport model, the improved model has the best coincidence with measurements, which can provide an accurate reference for the design of high-voltage direct current insulation systems.

Whitehead memorial lecture: Challenging defects of high voltage insulation systems

The primary function of a high voltage insulation system is to prevent the flow of electric current between oppositely charged conductors, facilitate heat transportation and provide mechanical support throughout a long service lifetime of more than 30 years. Therefore, high voltage insulation systems typically constitute combinations of different insulators, including interfaces between gases, oils and solids. Such composite systems are difficult to describe in terms of the characteristics of each individual component, and efforts to improve manufacturing and refine the quality of insulating materials are therefore often based on an empirical approach. In this paper, a more fundamental physical approach is taken, aiming to explain how engineering properties such as electrical breakdown, conduction, dielectric loss and endurance are affected by various types of impurities and additives.