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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]

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]

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]