Search Results (6)

In this contribution, a mathematical model allowing for the prediction of the AC surface arc propagation on polluted insulators under non-uniform electric field is proposed. The approach is based on the experimental concept of Claverie and Porcheron. The proposed model, which makes it possible to reproduce the surface electric discharge, includes a condition for arrest of the propagating discharge. The electric field at the tip of the discharge is the key parameter governing its random propagation. A finite element approach allows for mapping of the electric field distribution while the discharge propagation process is simulated in two dimensions. The voltage drop along the arc discharge path at each propagation step is also taken into account. The simulation results are validated against experimental data, taking into account several electro-geometric parameters (distance between electrodes, pollution conductivity, radius of high-voltage electrode, length of the plane electrode). Good agreement between computed and experimental results were obtained for various test configurations. Full article

The aim of this study is the presentation of the results of an in-lab comparative study of electrical and thermal monitoring of artificially polluted, HTV-textured silicone rubber insulators, with different pollution levels. This work is a preliminary study of an in-situ monitoring of 400 kV SiR textured in a polluted environment. The results showed that the rms leakage current magnitude and pulses, and the average dissipated power depended on the pollution levels and the dry-bands formation. The discharge activity and their nature are governed by the pollution level and the voltage. A differentiation and a quantification between dry-band discharge onset and dry-band arc inception is highlighted. Full article

The authors seek to investigate the characteristics of outage-causing faults that can be observed in a short time frame after their occurrence: waveform of the voltages and currents. The aim is to identify which characteristics can be used to estimate the failure type immediately after its occurrence. This paper lays the groundwork to determine which features display a stronger relation to four failure types with the aim of using this information in a later work, not presented in this paper, aimed at designing a reliable failure type estimator from readily available data. This paper focuses on the most common failures of the underground cable MV networks in Portugal: cable insulation; cable joint; secondary substation busbar; and excavation-motivated failures. A set of 206 waveform records of real underground MV network failures was available for analysis. After investigating the waveforms, the authors identified seven waveform characteristics which can be used for failure type estimation. Fault type characteristics can be used to distinguish secondary substation failures from the remaining failure types. Fault evolution does not yield relevant information. Fault self-extinction phenomenon was not observed in excavation-caused failures. There are differences for self-extinction characteristics between secondary substation busbar failures and the cable insulation and joint failures. Fault inception instant and arc voltage are two characteristics which are shown to have a promising merit to the identification process of failure types. Finally, fault intra-cycle repetitive extinction results have been found to be very similar for cable insulation failures and joint failures, but otherwise different regarding the remaining failure types. Full article

Series arc faults are challenging to detect in low-voltage dc (LVDC) distribution systems because, unlike other fault types, series arc faults result in only small changes in the current and voltage waveforms. Though there have been several approaches proposed to detect series arc faults, each approach has its requirements and limitations. A step change in the current and voltage waveforms at the arc inception is one of the characteristic signatures of these faults that can be extracted without requiring one to sample the waveforms at a very high frequency. This characteristic feature is utilized to present a novel approach based on voltage differential protection to detect series arc faults in LVDC systems. The proposed method is demonstrated using an embedded controller and experimental data that emulate a hardware-in-the-loop (HIL) test environment. The successful detection of series arc faults on two sets of series arc fault experimental data validated the approach. The results presented also illustrate the computational feasibility in implementing the approach in a real-time environment using an embedded controller. In addition, the paper discusses the robustness of the approach to load changes and loss of time synchronization between measurements at the two terminals of the line. Full article

There is a vital need to understand the flashover process of polymeric insulators for safe and reliable power system operation. This paper provides a rigorous investigation of forecasting the flashover parameters of High Temperature Vulcanized (HTV) silicone rubber based on environmental and polluted conditions using machine learning. The modified solid layer method based on the IEC 60507 standard was utilised to prepare samples in the laboratory. The effect of various factors including Equivalent Salt Deposit Density (ESDD), Non-soluble Salt Deposit Density (NSDD), relative humidity and ambient temperature, were investigated on arc inception voltage, flashover voltage and surface resistance. The experimental results were utilised to engineer a machine learning based intelligent system for predicting the aforementioned flashover parameters. A number of machine learning algorithms such as Artificial Neural Network (ANN), Polynomial Support Vector Machine (PSVM), Gaussian SVM (GSVM), Decision Tree (DT) and Least-Squares Boosting Ensemble (LSBE) were explored in forecasting of the flashover parameters. The prediction accuracy of the model was validated with a number of error cost functions, such as Root Mean Squared Error (RMSE), Normalized RMSE (NRMSE), Mean Absolute Percentage Error (MAPE) and R. For improved prediction accuracy, bootstrapping was used to increase the sample space. The proposed PSVM technique demonstrated the best performance accuracy compared to other machine learning models. The presented machine learning model provides promising results and demonstrates highly accurate prediction of the arc inception voltage, flashover voltage and surface resistance of silicone rubber insulators in various contaminated and humid conditions. Full article

Silicone rubber insulators are replacing the conventional ceramic and porcelain insulators rapidly in power transmission and distribution industry. Very limited field knowledge is available about the performance of silicone rubber insulators in polluted and contaminated environments and therefore need further investigation. A comprehensive analysis of silicone rubber sheets (intended for coating outdoor insulators) was carried out in this paper based on experimental results. The main performance parameters analyzed were arc inception voltage and flashover voltage. Dependence of these parameters on equivalent salt deposit density (ESDD), non-soluble salt deposit density (NSDD), relative humidity, ambient temperature, fog rate, dry band formation, dry band location and number of dry bands were investigated extensively. Insulator orientation and its effect on performance were also studied. The authors believe that this paper will provide a comprehensive knowledge about the flashover characteristics of silicone rubber insulators under humid, contaminated and dry band conditions. These results could be used in the selection and design of silicone rubber insulators for polluted environments. Full article