Search

The most monitoring techniques of metal oxide surge arresters are based on harmonics analysis of resistive leakage current. Therefore, the extraction of resistive component from total leakage current is required. Up to now, several methods have been proposed for resistive leakage current extraction such as time-delay addition method. This method is only able to separate accurate resistive component under pure sinusoidal applied voltage. An improved time-delay addition method is proposed in this paper which is able to extract surge arrester resistive component under applied harmonic voltage. To accomplish this purpose, experimental tests have been performed on 20 kV surge arrester and obtained voltage and leakage current signals have been applied to the proposed and previous algorithms for resistive component extraction. In addition to investigate the ability of proposed algorithm, a typical surge arrester has been simulated in ATP-EMTP software and total leakage currents have been applied to both algorithms for resistive leakage current extraction. Results show that the proposed method is able to extract resistive component under applied harmonic voltage more accurate than previous one. Therefore, it is an improvement with respect to previous one that only works for sinusoidal voltage. [ABSTRACT FROM AUTHOR]

Metal-oxide surge arresters (MOSAs) are essential equipments for power system protection and devices from lightning and switching transient overvoltages. Therefore, their operating condition and diagnosis are very important. In this study, a multi-layer support vector machine (SVM) classifier has been used for MOSA conditions monitoring based on experimental tests. Three features are extracted based on the test results for determining surge arresters operating conditions including clean virgin, ultraviolet (UV) aged clean surface, surface contaminations after and before UV housing ageing, and degraded varistors along active column. Then, the multi-layer SVM classifier is trained with the training samples, which are extracted by the above data processing. Finally, the five fault types of surge arresters are identified by this classifier. The test results show that the classifier has an excellent performance on training speed and reliability which confirm the high applicability of introduced features for correct diagnostic of surge arresters conditions. [ABSTRACT FROM AUTHOR]

Electro-thermal model of metal oxide surge arrester based on adaptive method has been proposed in this paper. Finite element method has been used to model surge arrester. Power loss, which is major parameter in electro-thermal analysis, has been estimated as a heat source in proposed model using artificial neural network and adaptive network based fuzzy inference system. In addition to voltage and temperature, operating history is also an important factor that must be considered in power loss estimation process. In order to formulize surge arrester performance history, degradation factor has been suggested as a new index in this paper. Therefore, voltage, temperature and degradation factor have been used as inputs in artificial models. So as to train the artificial neural network and adaptive network based fuzzy inference system, experimental results have been obtained by laboratory tests on new and utilized surge arresters varistors. In this regards, high voltage experimental setup, chamber and an oven have been prepared to acquire modeling data. Also, the results of infrared thermal camera have been used to validate the results of proposed adaptive electro-thermal model. Moreover, convection and radiation as effective factors in surface to ambient heat transfer have been studied and compared. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to estimate power loss characteristics of metal oxide surge arresters. The method was used to compute surge arrester power loss curve based on adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN). Surge arrester operating history is an important factor that bears influence on its power loss. Degradation was, in this paper, introduced as a new index to represent operating history of metal oxide surge arrester. Therefore, applied voltage, temperature and degradation factor were considered as inputs in ANFIS system and ANN models to obtain accurate power loss which is a very important factor in surge arrester thermal stability. Degrading effect was undertaken by measurement voltage and current in varistors degraded by utilization in network. The results of the two artificial models were compared. Results show that ANFIS was more accurate than ANN. This study shows that the power loss characteristics of surge arrester are to a great extent accurately predictable using proposed artificial model. [ABSTRACT FROM AUTHOR]

Metal oxide surge arresters are important equipments that apply to power system for protection against switching and lightning over-voltages. Surge arrester monitoring technique based on leakage current analysis is a conventional monitoring method. Lack of proper features and thresholds is most important limitation of this method. In this paper, new monitoring indexes for surge arrester diagnostic are proposed to determine surge arrester condition under different situations including clean condition, surface contamination and ultraviolet aging. The experimental tests have been performed on polymer surge arresters to evaluate the ability of the proposed indexes for distinguishing the operating condition of surge arresters. Results have been shown the viability of the new indexes on surge arrester monitoring procedures. The investigation and the discussion of the produced results can give good ideas the electric engineers in order to easily analyze arresters’ condition leading to effective schedule maintenance. [ABSTRACT FROM AUTHOR]

This paper presents proper indicators for evaluation of the surge arrester condition based on the leakage current analysis. Maximum amplitude of fundamental harmonic of the resistive leakage current ( I mr1 ), maximum amplitude of third harmonic of the resistive leakage current ( I mr3 ) and maximum amplitude of fundamental harmonic of the capacitive leakage current have been used as indicators for surge arrester condition monitoring. The effects of operating voltage fluctuation, third order of voltage harmonic, over-voltage and varistor aging have been investigated to show the ability of introduced indicators for accurate diagnostic of arrester’s conditions. In order to have accurate values of stray capacitors, 3D-FEM has been used. Moreover, surge arrester model for calculating leakage current has been performed in ATP–EMTP. In addition, the signal processing has been done using MATLAB software. This study shows that the introduced indicators are proper criteria for surge arrester condition monitoring. [ABSTRACT FROM AUTHOR]