The Discrimination of Fault Type by Unsupervised Neural Network

자율 학습 신경회로망을 이용한 고장상 선은 알고리즘

The direction and the type of a fault on a transmission line need to be identified rapidly and correctly, The work described in this paper addresses the problem encountered by a conventional algorithm in a fault type classification in double circuit line, this arises due to a mutual coupling and CT saturation under the fault condition. We present an approach to identify fault type with novel neural network on double circuit transmission line. The neural network based on combined unsupervised training method provides the ability classify the fault type by different patterns of the associated voltages and currents.