Ensemble learning based transmission line fault classification using phasor measurement unit (PMU) data with explainable AI (XAI).

ABSTRACT: A large volume of data is being captured through the Phasor Measurement Unit (PMU), which opens new opportunities and challenges to the study of transmission line faults. To be specific, the Phasor Measurement Unit (PMU) data represents many different states of the power networks. The states of the PMU device help to identify different types of transmission line faults. For a precise understanding of transmission line faults, only the parameters that contain voltage and current magnitude estimations are not sufficient. This requirement has been addressed by generating data with more parameters such as frequencies and phase angles utilizing the Phasor Measurement Unit (PMU) for data acquisition. The data has been generated through the simulation of a transmission line model on ePMU DSA tool

SUBMITTER: Bin Akter S 

PROVIDER: S-EPMC10861062 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature