Feature analysis in time-domain and fault diagnosis of series arc fault

Abstract

In order to monitor series arc fault in real-time for electrical connectors and improve the reliability of power supply systems, series arc fault experiments were carried out using an arc fault generator. A three-phase asynchronous motor and a three-phase frequency conversion motor were used as experimental loads. The variance, covariance and number of zero-crossing points of five adjacent periods of current signals were extracted and normalized. The feature vector was constructed by using the above variables such as number of zero-crossing points, variance and covariance. The k-nearest neighbor method was used for pattern recognition of the feature vector. The results showed that this method was effective for the diagnosis of series arc fault in electrical connectors.