Title
Transmission Line Fault Diagnosis Based on Wavelet Packet Analysis and Convolutional Neural Network
Abstract
Accurate classification of fault types for highvoltage transmission lines is a prerequisite for fault location and fault recovery. To this end, this paper proposes a fault classification method based on wavelet packet analysis and convolutional neural network. Firstly, according to the difference between the three-phase voltage and current wavelet energy vectors in the line fault, the samples representing different fault categories are constructed, then, the sample of the fault category is reconstructed into a grayscale image by the wavelet energy probability vector. The sample is trained by the convolutional neural network algorithm to obtain a CNN model that identifies different fault types. The simulation results show that the method has fast recognition speed and fault recognition accuracy is not affected by parameters such as fault resistance, fault location and system operation mode. It has strong practicability and reliability.
Year
DOI
Venue
2018
10.1109/CCIS.2018.8691304
2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)
Keywords
Field
DocType
Transmission line,Fault diagnosis,CNN,Wavelet packet analysis
Pattern recognition,Transmission line,Computer science,Convolutional neural network,Voltage,Real-time computing,Electric power transmission,Artificial intelligence,Probability vector,Grayscale,Wavelet packet analysis,Wavelet
Conference
ISSN
ISBN
Citations 
2376-5933
978-1-5386-6005-8
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Daohao Wang100.34
Dongsheng Yang2203.67
Zhou Bowen3182.69
Min Ma4197.30
Hongyu Zhang5182.66