Convolutional Neural Network Based Fault Detection for Transmission Line

Anshuman Bhuyan, Basanta K. Panigrahi, Kumaresh Pal, Subhendu Pati

20222022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)13 citationsDOI

Abstract

Faults are becoming more common as the number of transmission lines grows progressively. The detection of faults must be quick and precise to do the least amount of harm to the power system. Convolutional Neural Networks (CNN) is one of the finest options for detecting faults in transmission lines. This paper presents a novel fault detection method based on Convolutional Neural Networks in which the current vs. time graph of all faults is used as input for the image classifier. For the input an image data has been generated with appropriate target values and given to the model. The model is trained and tested after it is created. The testing results reveal that the convolutional neural network performs well for all types of faults.

Topics & Concepts

Convolutional neural networkComputer scienceArtificial intelligencePattern recognition (psychology)Classifier (UML)Fault detection and isolationTransmission lineElectric power transmissionArtificial neural networkContextual image classificationFault (geology)Image (mathematics)EngineeringTelecommunicationsGeologyActuatorSeismologyElectrical engineeringPower Systems Fault DetectionIslanding Detection in Power SystemsPower Transformer Diagnostics and Insulation