Dielectric Insulation in Medium- and High-Voltage Power Equipment—Degradation and Failure Mechanism, Diagnostics, and Electrical Parameters Improvement

The proper operation of medium- and high-voltage power equipment is greatly affected by the degradation of its insulation. There are five main factors that cause insulation degradation: electrical, mechanical, and thermal exposures, as well as chemical aggression and environmental pollution. Therefore, it is very important to be able to control the development of insulation material degradation and thus reduce the possibility of medium- and high-voltage power equipment failures. The diagnostic tests carried out to this end help to eliminate damage to the insulating components of power equipment. This effectively reduces or even eliminates catastrophic failures and thus reduces the risk of environmental pollution. This Special Issue addresses topics related to the measurement of and the use of sensors and other solutions to monitor the condition of power equipment components. Issues related to the determination of degradation and damage mechanisms of dielectric insulation are presented, as are ways to monitor insulating elements of power equipment, including the condition of solid, liquid, and gaseous insulation.

The Special Issue “Dielectric Insulation in Medium- and High-Voltage Power Equipment—Degradation and Failure Mechanism, Diagnostics, and Electrical Parameters Improvement” has received good responses. Of 16 submissions, 12 were accepted, which gives a 75% acceptance rate. This Special Issue does not include review papers; all accepted papers are original research papers.

Z. Pu et al. in paper [1] present a solution to the problem of winding turn-to-turn breakdown faults in dry-type transformers on wind farms under overvoltage conditions. For this purpose, a simulation model based on the structural dimensions and material parameters of transformer windings is developed. The winding distribution parameters are calculated using the finite element method. The simulation results show that the maximum overvoltage between turns of the transformer winding under lightning shock is 5.282 kV; the maximum overvoltage between turns of the winding under very fast transient overvoltage is 11.6 kV and occurs between the first two and three layers of the section, close to the insulation breakdown margin. The optimization of the proposed process is carried out. The accuracy of the winding structure optimization simulation study is verified by testing the transformer’s impulse voltage before and after optimization, providing a reference for the stable operation of dry-type transformers in practical wind farm applications.

Paper [2] focuses on issues related to the problems generated when the interruption of a vacuum circuit breaker on a wind farm causes high amplitude and rapid overvoltages, damaging the transformer’s inter-winding insulation. Based on the physical process of dielectric recovery during the opening of the vacuum circuit breaker, a model of dielectric strength recovery is built to simulate arc reignition. The simulation results show that the overvoltage amplitude and reignition times calculated by the model are closer to the measured values. Compared with the traditional linear curve reignition model, the accuracy was increased by 24% and 51.2%, respectively. A suitable suppression scheme is proposed by installing a combined arrester on the high-voltage side of the transformer and connecting a choke coil in series, which can limit the phase-to-ground and phase-to-phase voltages and reduce voltage steepness.

X. Ge et al. [3] present four insulation resistance (IR) degradation models for cross-linked polyethylene-insulated cables under thermal ageing. IR is an essential metric indicating the insulation conditions of extruded power cables. In the paper, the influences of methodologies and temperature profiles on IR simulation are evaluated. Cable cylindrical insulation is first divided into sufficiently small segments whose temperatures are simulated by jointly using a finite volume method and an artificial neural network to model the thermal ageing experiment conditions. The thermal degradation of IR is then simulated by dichotomy models that randomly sample fully degraded segments based on an overall insulation ageing condition estimation and discretization models that estimate the gradual degradation of individual segments, respectively. The insulation resistance simulation results are not only compared between different models, but also discussed in terms of the sensitivity of insulation resistance simulation to segment sizes and degradation rates.

X. Yi et al. [4] investigate the deterioration of and the abnormal temperature rise in the GFRP core rod material of compact V-string composite insulators subjected to prolonged alternating flexural loading under wind-induced stresses. The axial stress on the GFRP (Glass-Fiber-Reinforced Plastic) core rod, resulting from transverse wind loads, is a focal point of the examination. By establishing a stress model and damage model, the paper simulates and computes the evolution of damage in the outer arc material of composite insulator core rods subjected to alternating flexural loads. The research study underscores the significance of understanding the ageing and decay-like fracture process of compact line V-string composite insulators and provides crucial insights for future research aimed at enhancing the material properties of composite insulator core rods.

In paper [5], M. Florkowski and M. Kuniewski present information about partial discharges (PDs) and the subsequent deterioration of electrical insulation caused by the high electric-field stress and high-frequency switching phenomena as well as the impact of environmental conditions. The authors describe a novel combined approach based on surface resistance and potential mapping to reveal the effects of internal processes and the deterioration of insulating material due to the actions of partial discharges. The following two-step approach is proposed: multi-point resistance mapping is used to identify the spots of discharge channels, pointing to surface resistance that is a few orders of magnitude lower as compared to untreated areas, whereas high spatial and temporal resolution allows for the precise mapping and tracing of decay patterns. Experiments were carried out on polyethylene (PE) and Nomex specimens that contained embedded voids. The presented methodology and experimental results expand our understanding of PD mechanisms and internal surface processes.

P. Mikrut and P. Zydroń in paper [6] present the conditions for the formation of PD pulses in gaseous voids located in the XLPE insulation of an HVDC cable. The MATLAB^® procedure and the coupled electro-thermal simulation model implemented in COMSOL Multiphysics^® software are used in the analysis. The FEM model was used to study the effect of the applied voltage, the temperature field, and the location of the gaseous void in the distribution and values of the electric field in the cable insulation. In the numerical simulation procedure, the time sequences of PDs arising in the gaseous defects of the HVDC cable insulation were analyzed by observing changes caused by the increase in the temperature of the cable core. The model was used to study the conditions for PD formation in models of three HVDC cables for DC voltages from 150 kV to 500 kV. The critical dimensions of gaseous voids— which, if exceeded, make voids sources of PDs—were also estimated for each of the analyzed cables.

K. Meresch et al. [7] present a study on the use of acoustic inspection to detect partial discharges. Ultrasonic sensors have made detecting partial discharges through acoustic sensing increasingly feasible. Interpreting acoustic signals can pose challenges, as it requires extensive expertise and knowledge of equipment configuration. To solve this problem, a technique based on zero-crossing rate and fundamental frequency estimation is proposed to standardize insulator diagnosis. In the experiment proposed by the authors, a database of 72 raw acoustic signals with frequencies from 0 to 128 kHz was used, and various types of contamination and defects were introduced into the chain of insulators. The proposed technique allowed for the detection of partial discharges and their classification according to type, such as corona or surface discharges. This method simplifies the process while providing valuable insights into the severity of the phenomena observed in the field.

Paper [8] by A. Cichon and M. Wlodarz presents measurements of the technical condition of an on-load tap changer (OLTC). The methods used require the transformer to be taken out of service for the duration of the diagnostic procedure for the sake of precision. This authors create an online OLTC diagnostics method based on acoustic emission (AE) signals. An extensive measurement database containing many frequently occurring OLTC defects is used, and a method of feature extraction from AE signals based on wavelet decomposition is developed. Several machine learning models for classifying OLTC defects are created, and the most effective model is selected.

E. Taine et al. [9] present research on silicone elastomers, which are commonly used in high-voltage engineering. They are used in outdoor insulation as coatings or structural elements, or at interfaces between network elements, such as cable sealing ends (CSEs). Developing reliable systems that operate under high electric fields and variable repeated strains requires a thorough understanding of the mechanisms behind electrical breakdown and its coupling to mechanical cycling. The effect of Mullins damage and mechanical fatigue on silicone elastomers is investigated in the paper. An electro-mechanical instability model that considers cyclic softening allows for predicting the evolution of breakdown strength depending on loading history.

P. Zukowski et al. in paper [10] present studies of the site percolation phenomenon for square matrixes with dimensions L = 55, 101, and 151 using the Monte Carlo computer simulation method. The study features an in-depth analysis of the test results using a metrological approach, which determines the uncertainty of estimating the iteration results with statistical methods. The authors establish that the statistical distribution of the percolation threshold value is a normal distribution. The average value of the percolation threshold for relatively small numbers of iterations varies in a small range. For large numbers of iterations, this value stabilizes and practically does not depend on the dimensions of the matrix. The value of the standard deviation of the percolation threshold for small numbers of iterations also fluctuates to a small extent. For a large number of iterations, the standard deviation values reach a steady state. Along with the increase in the dimensions of the matrix, there is a clear decrease in the value of the standard deviation. The application of the metrological approach to the analysis of the percolation phenomenon simulation results allows for the development of a new method of optimizing the determination and reducing the uncertainty of percolation threshold estimation. It consists in selecting the dimensions of the matrix and the number of iterations in order to obtain the assumed uncertainty in determining the percolation threshold. Such a procedure can be used to simulate the percolation phenomenon and to estimate the value of the percolation threshold and its uncertainty in matrices with other matrix shapes than square ones.

Paper [11] focuses on determining the percolation phenomenon for square matrices using the Monte Carlo simulation method. The spatial distributions of the coordinates of the nodes creating the percolation channel are determined, and maps of the density distribution of these nodes are created. It is established that in matrices with finite dimensions, an edge phenomenon occurs, consisting of a decrease in the concentration of nodes creating a percolation channel as one approaches the edge of the matrix. As the matrix dimensions increase, the intensity of this phenomenon decreases. Clusters whose dimensions are close to half of the matrix dimensions are most likely to occur. The research shows that both the values of the standard deviation of the percolation threshold and the intensity of the edge phenomenon are clearly related to the dimensions of the matrices and decrease as matrix dimensions increase.

In paper [12], an in-depth analysis of the percolation phenomenon for square matrices with dimensions from L = 50 to 600 for a sample number of 5 × 10⁴ is performed using Monte Carlo computer simulations. The percolation threshold value is defined as the number of conductive nodes remaining in the matrix before drawing the node interrupting the last percolation channel, in connection with the overall count of nodes within the matrix. The dependencies of the expected value of the percolation threshold and the standard deviation of the dimensions of the matrix are determined. It is established that the standard deviation decreases with the increase in matrix dimensions. The analysis involves not only the spatial distributions of nodes interrupting the percolation channels but also the overall patterns of node interruption in the matrix. The distributions reveal an edge phenomenon within the matrices, characterized by the maximum concentration of nodes interrupting the final percolation channel occurring at the center of the matrix. Increasing the dimensions of the matrix slows down the rate of decrease in the number of nodes towards the edge. In doing so, the area in which values close to the maximum occur is expanded. The approximation of the experimental results allows for the determination of formulas describing the spatial distributions of the nodes interrupting the last percolation channel and the values of the standard deviation from the matrix dimensions.