Research Article
Transmission Power Line Fault Detection using Convolutional Neural Networks
@INPROCEEDINGS{10.4108/eai.7-6-2021.2308661, author={Kalanidhi K and Baskar D and Vinod Kumar D}, title={Transmission Power Line Fault Detection using Convolutional Neural Networks }, proceedings={Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India}, publisher={EAI}, proceedings_a={I3CAC}, year={2021}, month={6}, keywords={transmission lines fault analysis cnn alexnet vgg16 resnet}, doi={10.4108/eai.7-6-2021.2308661} }- Kalanidhi K
Baskar D
Vinod Kumar D
Year: 2021
Transmission Power Line Fault Detection using Convolutional Neural Networks
I3CAC
EAI
DOI: 10.4108/eai.7-6-2021.2308661
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.
