Search

The V-I characteristic of ZnO varistor which has been obtained from experimental test, is an important parameter to model surge arrester electrical behavior. Generally, in grid voltage, this characteristic depends on temperature variations. In this paper, an improved metal oxide surge arrester electrical model has been proposed under grid voltage. Temperature effects on surge arrester performance have been considered in the proposed model. Artificial neural network has been used to determine V-I characteristic at temperature variations. Therefore, in proposed model, the V-I characteristic of each varistor has been estimated based on applied voltage and operating temperature. To validate proposed model, laboratory setup and infrared thermal camera have been arranged and experimental tests results have been compared with simulation ones. Results revealed that there has been a good agreement between simulation and experimental results. Moreover, electrical behavior of surge arrester is predictable with high precision through the proposed artificial model. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive model for electro-thermal analysis of metal oxide surge arresters based on finite element method. The input of the proposed model has been derived from power loss curve using adaptive network based fuzzy inference system (ANFIS). Moreover, degradation factor has been introduced as a new index to represent operating history of metal oxide surge arrester. Therefore, applied voltage, temperature, and degradation factor have been considered as inputs in the ANFIS model to obtain accurate power loss which is of paramount importance in electro-thermal analysis. High voltage experimental setup and an oven have been prepared to acquire data for the proposed model. Degrading effect has been undertaken by measurement of the voltage-current characteristic of used and degraded varistors. The effect of assembling process, in addition, has been considered using different virgin (not degraded) varistors in experimental measurements. In addition, according to the IEC standard, the aging effect has been obtained experimentally by putting the varistor under 115 °C and continuous operating voltage for 1000 h. To validate the results of the electro-thermal model, thermal infrared camera has been used under different applied voltages. Obtained results show that the electro-thermal behavior of surge arrester is predictable with high precision through the proposed artificial model. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study proposes a new method for modelling of metal-oxide surge arresters (MOSAs) thermal balance diagram based on power loss and heat dispersion curve. Adaptive network-based fuzzy inference system (ANFIS)- based method was offered to compute surge arrester power loss characteristic. Thermal stability (TS) of surge arrester, which is analysed by thermal balance diagram, has a great influence on their proper functions. MOSA material variation is an important factor that has an influence on its TS. Degradation factor (DF) was introduced as a new index in this study to represent material variations of MOSA. Therefore, applied voltage, temperature and DF were considered as inputs in ANFIS model to obtain accurate power loss which is a very important factor in TS characteristic. Heat dispersion curve was obtained as a main feature in the thermal balance diagram from surge arrester cooling curve. Degrading effect was considered using voltage and current measured from MOSA which are degraded by utilisation in the network. In addition, effect of assembling process has been considered by using new different varistors in experimental measurements. This study shows that the thermal balance diagram of surge arrester is predictable with a good accuracy by using artificial model. [ABSTRACT FROM AUTHOR]

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]

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 one of the most important equipments for power system protection against switching and lightning over-voltages. Surge arresters leakage currents increase by the operating time. In this study, the important factors on leakage current variations such as surface contamination, ultraviolet ageing and varistor degradation have been studied. To accomplish this purpose, experimental tests have been performed on various polymer housed surge arresters. Fast Fourier transform analysis has been performed on measured leakage currents. Results show that ultraviolet radiation and varistor degradation affect resistive harmonic components, especially the third and fifth harmonics. Moreover, it is observed that surface contamination has influence on fundamental harmonic variation more than ultraviolet ageing. In addition, combination of ultraviolet radiation and pollution had more effect on leakage currents. The investigation and the discussion of the results can be used to easily analyse arresters condition leading to effective schedule maintenance. [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]