Leakage Current Of Insulators Research Articles

Analysis of electric field and leakage current of glass and porcelain insulators under clean and polluted conditions: A comparative study of three profiles

Electric field (EF) enhancement in insulating systems may occur due to both internal and external factors including inefficient design, manufacturing defects, and depositions of pollutants. Consequently, phenomena like partial discharge, unexpected flashover, and even breakdown of the insulator may take place. In this work, aiming to suggest relatively better performance, a comparative study of porcelain and glass insulators under clean and polluted conditions was conducted using finite element-based software, COMSOL Multiphysics. Three profiles of both the insulators namely, standard, anti-fog, and open were modelled, and the effects of their geometry and varying electric conductivity of the surrounding air were analyzed on EF distribution and leakage current (LC). These parameters were also investigated under the deposition of water droplets and various pollution scenarios. Results of the performed computations demonstrated that by making air conductivity high, LC enhanced significantly while EF was affected the least. The presence of water droplets in the vicinity of live electrode and deposition of pollution near the end fittings created the strongest impact on EF distribution. Among the studied configurations, standard profile of porcelain material appeared to be the most promising for suppressing effects of the simulated stresses.

Leakage Current Harmonic Index as A Diagnostic Technique for Tool to Assess the Surface Condition Assessment of Aged Insulator Surface Under Varying Humidity

Leakage current has been widely utilized as an effective means to monitor the pollution severity of outdoor insulators. The indices of the odd harmonic components of the leakage current have proven to be reliable indicators of insulator performance, particularly in the third to fifth harmonic orders. However, significant challenges exist in using these indices to monitor and classify the leakage current of aged insulators under varying harmonic and pollution conditions. This paper introduces the harmonic index K((3/(5+7) ) which employs third up to seventh odd harmonic component of the leakage current as a diagnostic tool to determine the pollution severity of aged insulators under light, medium and heavy contamination levels. The experiments were conducted using 12 different sets of 11kV insulators with varying degrees of aging. The results indicate that the harmonic index Ki for new and moderately aged insulators decreases with an increase in contamination and is lower than that of heavily aged insulators.

Novel criteria for silicone rubber insulators condition monitoring based on leakage current analysis: Considering asymmetric aging and pollution

Insulators' flashover due to pollution is the biggest challenge of power systems. This phenomenon will become more intense with the aging of the insulator. Precise and prompt determination of the severity of pollution and the insulator status in terms of aging is essential for preventing pollution flashovers. Therefor monitoring insulator leakage current is essential for preventing the pollution flashover phenomenon. This article proposes innovative criteria based on the insulator's leakage current analysis for the classification of aging and pollution severity. The leakage current signal of the virgin, fully aged, and asymmetric aged insulators have been examined under different pollution conditions. Based on the experimental results analysis, two new criteria have been proposed for assessing the silicon rubber insulator condition which have resulted from the first-order harmonic of leakage current (It1), the third and fifth-order harmonics ratio of leakage current (It3/5), and the cosine of first-order harmonic phase angle (PI). Analyzing the proposed criteria demonstrates that two proposed criteria have a strong correlation to aging and pollution, respectively. These two new criteria can be implemented to identify the early signs of pollution and aging of insulators. The ability of proposed criteria has been established based on random forest, support vector machine, decision tree algorithm, multi-layer perceptron, and decision-making level fusion in Python language. The obtained criteria based on experimental data in different conditions have been employed for classifier training which can be applied in industrial settings without the need for laborious data labeling, ensuring fast and accurate outcomes.

The Effect of Conductivity on Surface Leakage Currents on Iced-Covered Insulators

Insulators on power transmission systems today are vital to ensuring the reliable and effective operation of high-voltage lines. However, it is inevitable that insulators will encounter problems such as contamination and icing due to natural environmental conditions and environmental factors. Pollution layers and ice buildup that have developed on insulator surfaces have a detrimental impact on insulation performance, increase surface leakage currents, and result in power line failures or interruptions. This article focuses on understanding the phenomena of contamination and icing in insulators, evaluating their effects, and investigating methods that can be taken to prevent or reduce these problems. For this purpose, solutions with different conductivity values were sprayed on the insulators in the laboratory environment, and ice covering the insulator surface was provided. Leakage currents were investigated by applying an alternating voltage with mains frequency to the insulators. Data were obtained with the help of the LabVIEW program, and the relationship of leakage currents of porcelain, glass, and silicone insulators with conductivity was compared.

PENGUJIAN TEGANGAN TEMBUS KABEL NYA INSTALASI LISTRIK TEGANGAN RENDAH

This research examines the breakdown voltage on low voltage cables, by testing the voltage to breakdown and insulation resistance on low voltage cables. The purpose of this study was to determine the maximum limit of the cable sample's ability to receive injection voltage before experiencing an electric breakdown and the impact on insulation resistance and cable leakage current when given a voltage exceeding normal voltage and then to analyze the highest voltage that the cable can withstand in a certain time. The parameters used in the research design are insulation resistance and leakage current. The test was carried out at the High Voltage Laboratory, State University of Gorontalo with a high voltage tester HV transformer brand Terco Sweden with specifications for a maximum voltage capacity of up to 100 kV. The cable samples used were NYA 2x2.5 cables with the brands eterna, suprime and sigeru.

Equivalent circuit for mine power distribution systems for the analysis of insulation leakage current

Successful mining businesses rely heavily on the safety and reliability of their mine power distribution systems. Mine power distribution systems are designed to withstand an aggressive environment with a range of hazards. The harsh operating conditions require improvement to personnel protection systems through the study and simulation of the electric network’s normal and emergency operations. The purpose of this study is to assess insulation leakage current using an equivalent circuit of a mine power distribution system. The simulation identified the key properties of the equivalent circuit which can model potential hazardous situations. We also selected the quantitative metrics and the equivalent circuit property ranges. In order to simulate the transients, we recommended using the time constants for the oscillation damping in circuits, the insulation phase resistance properties, and the absorption currents. This paper presents the equations to estimate these values. As an example, we considered the equivalent circuit of a mine power distribution system with an R-L filter in the residual current device line. The equivalent circuit helps analyze the current leakage when a person touches a live phase conductor accounting for low-frequency polarization in the phase insulation. The proposed approach to the simulation and analysis of the insulation current makes it possible to generate an equivalent circuit of the mine power distribution system to analyze phase voltage asymmetry, trip currents of residual current devices, low-frequency polarization of the insulation, and the leakage current effects on the human body.

Flashover Prediction of Polymer Insulators Based on Dynamic Modeling of Pollution Layer Resistance Using ANN

In this article, a novel dynamic approach is proposed to analyze pollution layer resistance of polymer insulators. One of the main challenges in dynamic modeling of pollution layer resistance is the effects of pollution layer conduction and pollution layer thickness on discharge length due to change in pollution layer thickness and wetting rate in real outdoor conditions. The represented approach investigates the effects of such parameters on variation of discharge length. Then, an artificial intelligence (AI) algorithm based on artificial neural network (ANN) is developed to predict flashover voltage (FOV) of polluted insulators according to the prediction of critical discharge length. Calculations are done based on experimental data of leakage current (LC) and voltage of insulators in different pollution conditions. Obtained results by the proposed approach show a close correlation with experimental results. In addition, various validation tests are performed to investigate the effects of hydrophobicity class (HC) of polymeric surface and pollution type on the quality of the represented analysis. Results of the validation also show a close correlation between calculations and real data.

Inception 1D-convolutional neural network for accurate prediction of electrical insulator leakage current from environmental data during its normal operation using long-term recording

Contamination flashover remains one of the biggest challenges for power grid designers and maintenance engineers. Insulator leakage current contains relevant information about their state so that continuous monitoring is considered the most effective way to prevent contamination flashover. In this work, we attempted to accurately predict insulator leakage current in real time during normal operations based on environmental data using long-term recordings.We first confirmed that the history of environmental data also contained relevant information to predict leakage current by conditional Granger analysis and determined that 20 was the optimal number of previous samples for this purpose. We then compared the performance of typical regression models and convolutional neural network (CNN), when using both current and the last 21 samples as input features. We confirmed that the model with the last 21 samples might perform significantly better. Input features pre-processing by cascaded inception architecture was fundamental to capture the complex dynamic interaction between environmental data and leakage current and significantly improved the model performance. CNN based on inception architecture performed much better, achieving an average R2 of 0.94 ± 0.03. The proposed model could be used to predict leakage current in both porcelain insulators with or without coatings and silicone composite insulators.Our results pave the way for creating an on-line pre-warning system adapted to individual installations, can anticipate the negative consequences of weather and/or pollution deposits and is useful for designing a strategic high-voltage electrical insulator preventive maintenance plan for preventing contamination flashover and thus increase power grid reliability and resilience.

Research on early warning method for pollution flashover based on phase angle of insulator leakage current

Pollution flashover is a systematic disaster of power system. It is significant to early warning insulator pollution flashover in real time and accurately. The phase angle of the insulator leakage current is a critical parameter for insulator pollution flashover early warning. However, in field applications, the operating voltage waveform cannot be obtained so that this characteristic parameter cannot be applied to the insulator pollution flashover early warning system. This paper proposes a method to obtain the phase angle of the leakage current of the online insulator. This method avoids the direct measurement of the operating voltage waveform, but uses the stray capacitance between the signal plate of the early warning device metal shell and the transmission line to obtain the operating voltage signal indirectly. In this paper, the method and the early warning mechanism of pollution flashover are analyzed theoretically, and then the early warning device for pollution flashover is designed. Finally, the effectiveness of the early warning device is verified by experiments.

Influence of Mounting Incline and Natural Environmental Factors on Pin-Type Silicone Rubber Insulator Leakage Currents Using Internet-Based Monitoring

Influence of Mounting Incline and Natural Environmental Factors on Pin-Type Silicone Rubber Insulator Leakage Currents Using Internet-Based Monitoring

Discharge development process and flashover performance of snow-accreted insulators under natural environment

The safe and reliable operation of power system in China is significantly affected by snow-covered insulator. At present, many studies on snow-accreted insulator have been undertaken by domestic and foreign scholars in the laboratory. However, whether the laboratory test is equivalent to the field test and whether it can be directly used to guide the external insulation design of transmission line still need to be evaluated. In this regard, AC flashover test of snow-coated insulator was carried out in the laboratory and field in this paper, and then the characteristics of AC discharge path and the variation of leakage current of snow-accreted insulator with time were obtained, and its flashover performance was compared and analysed. The research results show that the amount of snow on the insulator gradually decreases from the stagnation point at the edge of the shed on the windward side to both sides, which is attributed to the uneven distribution of the local collision coefficient of snow crystal particles on the insulator surface. The arc is yellow during the flashover of snow-accreted insulator in the artificial climate chamber. While in the natural environment, the discharge arc appears blue and purple, and the phenomenon of arc levitation and snow layer shedding can be observed. For composite insulator FXBW-35/70 covered with different degrees of snowing in the natural environment, the flashover path develops along the creepage distance in case of the slight snowing, while the path propagates over the dry arc distance under severe snowing condition. Consequently, the flashover voltage decreases by 48.7%. There are many discrepancies between different types of the insulators, such as the creepage distance, dry arc distance and other structural parameters, and the number of air gap after snowing, which affect arc dynamic development properties. Hence, the electrical performance of composite insulator is better than that of glass insulator, and the snowing flashover voltage of the former is 5.56–9.47% higher than that of the latter.

Insulator Leakage Current Prediction Using Hybrid of Particle Swarm Optimization and Gene Algorithm-Based Neural Network and Surface Spark Discharge Data.

This study proposes a new superior hybrid algorithm, which is the particle swarm optimization (PSO) and gene algorithm (GA)-based neural network to predict the leakage current of insulators. The developed algorithm was utilized for the online monitoring systems, which were completely installed on the 69 kV and 161 kV transmission towers in Taiwan. This hybrid algorithm utilizes the local meteorological data as input parameters combined with the extracted enhanced data: the percentage of spark discharge areas and the brightness change in the image of the discharge phenomenon. These data with a high correlation with the leakage current are utilized as input vectors to improve the accuracy and effectiveness of the developed hybrid model. The performance of the developed algorithm is compared with a traditional PSO-based neural network and backpropagation neural network (BPNN) to evaluate and analyze. The comparative simulation results prove the effectiveness of the combination of hybrid PSO-GA-based neural network and surface discharge data, which achieved a maximum improvement of 38.54% MSE, 10.62% MAPE, and 3.41% R square for 161 kV data and 39.28% MSE, 12.62% MAPE, and 1.61% R square for 69 kV data. Moreover, the data with enhanced inputs outperform the traditional data in most benchmark factors, improving the accuracy and effectiveness in defining the deteriorative insulators. The developed methodology with a noticeable improvement was utilized in the online monitoring system to reduce the operational and maintenance cost of transmission lines in Taiwan Power Company.

The Effect and Compensation of Phase Angle Deviation Along the Winding for the Online Stator Insulation Condition Monitoring

The insulation impedance monitoring methods such as capacitance/dissipation factor monitoring methods have been proposed for the online stator insulation condition monitoring. By measuring the insulation leakage currents and the phase-to-ground voltages, the groundwall and phase-to-phase insulation impedances can be calculated in frequency domain. In the existing methods, the calculation is based on the assumption that the differential mode phase-to-ground voltage decreases linearly along the stator winding and no phase angle deviation is considered. However, the phase angles of the induced electromagnetic forces in stator winding in different slots depend on the slot positions. The assumption in the existing methods neglects this phase deviation and causes errors in the monitoring results. In order to eliminate this effect, the effect of the phase angle deviation on monitoring results is analyzed in this article. Its dependency on the turn number, the winding configuration and the frequency of the selected monitoring signals is studied. A compensation method is proposed and the compensation coefficients are calculated for different winding cases for the demonstration purpose. The online experimental results prove that the accuracy of the online monitoring results is improved after the compensation.

Overhead Lines and Wildfires: Role of Outdoor Insulators: Prepared by a Task Force of the IEEE DEIS Outdoor Insulation Technical Committee

Insulator leakage current is the primary root cause of pole fires; therefore, it is critical to recognize outdoor insulators as potential cause of wildfires.

An LLC Converter with Capacitive Insulation

Offline power converter products must apply for and pass the national electrical safety code before they can be marketed. The transformers of offline power converters must be made from certificated materials with a high voltage rating and high electrical insulation, which increases the volume of the transformers and the printed circuit boards. In most studies, the miniaturization of a power converter is usually achieved by increasing the conversion efficiency and reducing the heat sink, or by increasing the switching frequency to reduce the size of the transformers. In this paper, the insulation material is reduced to miniaturize the transformer of the LLC converter. The resonant capacitor of the LLC converter is used to meet the requirements of insulation voltage, leakage current, creepage, and clearance. A prototype with the specifications of 12 V and 10 A rated output was built to verify the proposed method.

Convolutional neural network bidirectional long short-term memory to online classify the distribution insulator leakage currents

In this project, the online monitoring systems to classify the leakage current of 15 kV and 25 kV distribution insulators were installed in mountain areas in Taiwan. The convolution neural network bidirectional Long Short-Term Memory (CNN-Bi-LSTM) was employed to categorize the leakage current levels based on the field experiences of the TaiPower company. The sequential weather factors and the different distribution insulators leakage current were collected every hour and utilized in the proposed model's learning process. In addition, a simple grid search parameter optimization was implemented to identify the optimum architecture of CNN-Bi-LSTM for each data collection. Moreover, the optimized CNN-Bi-LSTM structure performances were evaluated and compared with the gated recurrent unit (GRU), LSTM, CNN-LSTM, and Bi-LSTM neural networks. The experiments results proved that the proposed CNN-Bi-LSTM improved the category cross-entropy error, the accuracy, and the precision metric with a maximum enhancement of 71.331%, 14.382%, and 14.042% in the training data and 91.250%, 17.529%, and 17.343% in the validating data, respectively. Moreover, the experiment outcomes in different data models prove that the 24-sequential weather parameters have a significant impact on the achievement of CNN-Bi-LSTM in categorizing the leakage current. The optimized CNN-Bi-LSTM structures were applied in the online monitoring systems to provide a better schedule for maintenance operations at the Taipower company.

Investigation on the Physical Properties of Varistors According to Temperature

This study aims to determine the basic physical properties of varistor, which is a protective element in a surge protection device (SPD) used to prevent damage to electric and electronic equipment due to lightning. To do this, we conducted tests of insulation resistance, leakage current, heating characteristics, accelerated aging, impulse current, and operating duty according to temperature. To measure insulation resistance and leakage current, an S1-1568 insulation resistance tester was used. To measure the temperature rise according to a change in the external environment, a convection oven was employed. For the impulse current test, a surge current generator of a direct lightning strike, which was designed and manufactured according to the IEEE and UL standards, was used. We verified that the leakage current increased and the insulation resistance decreased as the voltage and outside temperature were applied to the varistor. This meant that heating and deterioration accelerated due to the leakage current, depending on the place where the SPD was applied. Heat accumulation due to constant heating could cause a change in the physical properties of a varistor and degradation of performance, leading to failure. The impulse current test results of the varistor through the operating duty test showed that changes in insulation resistance and leakage current did not occur significantly with an increase in the number of impulse applications when the less than the maximum surge currents. On the other hand, the maximum surge current was exceeded, the insulation resistance rapidly decreased, which also rapidly increased the leakage current.

Dynamic modeling of flashover of polymer insulators under polluted conditions based on HGA-PSO algorithm

This paper investigates the pre-flashover conditions of polymer insulators under polluted condition. This investigation is done by combination of analytical mathematical formulations, which were deduced in previous researches with AI based on HGA-PSO. The aim of this novel approach is to find proper correlation between experimental data of voltage and leakage current of insulators to theoretical formulations. Correlations are obtained and compared between discharge resistance and leakage currents for different cases of the pre-flashover conditions. Some experiments on real medium voltage polymer insulators under polluted conditions are done and real-time data of voltage and leakage currents are recorded as the voltage is increased step-by-step until flashover occurs. The experimental data are used by the proposed approach to better understand the pollution flashover behavior. Intelligent approach can be traced through a proposed flowchart which illustrates calculation procedure from the beginning point to the final point of identification of the pre-flashover conditions. Results of the intelligent algorithm show proper correspondence to the analytical formulations. In addition, the proposed algorithm is able to predict pre-flashover conditions properly using ANN classification. The proposed procedure can be an effective tool to develop an online monitoring system in order to prevent pollution flashover of polymer insulators.

Mapping Relation of Leakage Currents of Polluted Insulators and Discharge Arc Area

A fundamental parameter of polluted insulator online monitoring is the leakage current, which has already been shown to be well-related to the pollution discharge of insulators. In this article, in an effort to quantitatively reflect the discharge intensity and the discharge status by the leakage current, we carried out an experimental study on artificial pollution discharge of insulators. A high-speed photographic apparatus was utilized to capture the entire process of local arcs on a porcelain insulator surface, including the arc generation, the arc development, and the flashover, for which the associated leakage current of insulators was synchronously digitized. A comparative analysis of the relation between the two-dimensional discharge image and the leakage current waveform in the process of arc generation and development shows that if the arc area on the insulator surface is relatively small and the leakage current passes through zero, the arc might completely become extinct, whereas this phenomena will not occur if the arc area is larger. In addition, the amplitude of the discharge arc area is found to be roughly proportional to the square of leakage current over the range of leakage current amplitude from 0 to 150 mA. Our results can provide an important guidance for judgment of the discharge status and the discharge intensity on insulator surfaces using the leakage current of insulators.

Monitoring method for leakage current of transmission line insulators under typical environmental conditions

There are many factors affecting insulator leakage current of transmission line, resulting in low accuracy of leakage current monitoring. This paper designs a monitoring method for insulator leakage current of transmission line under typical environmental conditions. The influence of different factors on the accuracy of leakage current monitoring is analyzed. Based on the fuzzy mathematics theory, the insulator operating characteristic quantity is analyzed, and the uncertain factors in the insulator operating characteristic quantity are calculated, so as to realize the insulator leakage current monitoring of transmission line under typical environmental conditions. The example analysis shows that the leakage current monitoring accuracy of the method studied in this paper is high in the case of no arc, local arc, adjacent flashover, pollution and saturated environment, which proves the effectiveness of the method studied.