Cable ground fault diagnosis based on current waveform by using support vector machine

Abstract

At present, a person is performing the determination of the ground fault appearance at the time of a distribution line failure, which requires time cost and human cost. In addition, there are changes that push up power transmission and distribution related costs, such as the introduction of renewable energy due to power system reforms, and the power transmission and distribution sector must reduce costs by improving efficiency. Therefore, there is a demand for automation by a machine for determining the appearance of a ground fault when a distribution line fails. Existing studies have reported the possibility of distinguishing between ground fault-like cable degradation and other ground fault-like features. Therefore, in this study, we will examine the discrimination of five other types of ground faults in the existing studies. The current waveforms of five types of ground faults were learned by a support vector machine, and their potential discrimination performance was evaluated by leave-one-out cross-validation. As a result, a high accuracy rate could be obtained in all classifications of ground faults.