Monitoring and identification of metal–oxide surge arrester conditions using multi-layer support vector machine


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 vital. In this study, a multi-layer support vector machine (SVM) classifier has been used for MOSA conditions monitoring based mostly on experimental tests. 3 features are extracted based on the test results for determining surge arresters operating conditions together with clean virgin, ultraviolet (UV) aged clean surface, surface contaminations when 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 on top of data processing. Finally, the five fault varieties of surge arresters are identified by this classifier. The take a look at results show that the classifier has an excellent performance on coaching speed and reliability that confirm the high applicability of introduced features for correct diagnostic of surge arresters conditions.

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