Machine learning-based fault diagnosis for high-voltage transmission lines

Abstract

In order to comply with the speed and diversification process of human economic and technological development, the power system gradually develops in the direction of intelligence and complexity, and it is urgent to combine transmission line fault diagnosis methods with machine learning pattern recognition problems in order to reflect the representative characteristics of transmission lines under different operating conditions and different operation modes. In this paper, a convolutional neural network (CNN) model is established for transmission line fault diagnosis, and a series of parameters such as weights, bias, learning rate, batch size and loss function of the proposed CNN model are debugged through experimental comparison, and the best CNN model structure is determined through experimental comparison and debugging, and the recognition accuracy is higher than other traditional machine learning algorithms, which can be as high as 98.38%, with validity, practicality and advancedness.