Effect of Sampling Variation in Accuracy for Fault Transmission Line Classification Application Based On Convolutional Neural Network

Transmission line is a crucial link between generation and consumer. They are mostly having high chances fault to happen due exposed to real world. In this paper, a machine learning algorithm based on Deep learning convolutional neural-network application to detection and classification fault in transmission line. Sampling variation effect accuracy and response time for classification. To increase accuracy system use voltage and current measurement. However, signal type which measurement in voltage and current increase input and network size so longer response time will be required.