Transmission Power Line Fault Detection using Convolutional Neural Networks

K, Kalanidhi and D, Baskar and Kumar D, Vinod (2021) Transmission Power Line Fault Detection using Convolutional Neural Networks. In: I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India.

[img]
Preview
Text (PDF)
eai.7-6-2021.2308661.pdf - Published Version

Download (865kB) | Preview

Abstract

In an electrical power system, most of the faults occurs in overhead transmission lines because of most of the conductor exposure to the atmosphere. Therefore, Insulated Overhead Conductors (IOCs) are widely used. To overcome this, a robust real-time PD fault analysis system is required. To analyze and classify the raw voltage signal for detection of PD's in IOC's a Convolutional Neural Network (CNN) based fault classification algorithm is proposed in this paper. The CNN is implemented using popular pre-trained CNN architectures such as AlexNet, VGG16 & ResNet are applied to the voltage signals in the dataset. From the values of Precision, Recall & F1-Score it is observed that ResNet architecture provides the best prediction and classification results.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: transmission lines fault analysis cnn alexnet vgg16 resnet
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor IV
Date Deposited: 21 Jun 2021 08:10
Last Modified: 21 Jun 2021 08:10
URI: https://eprints.eudl.eu/id/eprint/3897

Actions (login required)

View Item View Item