Fault Classification of High Voltage Transmission Line Based on Convolutional Neural Network

Ye, Wanyu; Jian, Shengchao; Ou, Ruiming; Huang, Shaochuan; Gong, Xiang; Peng, Xiangang; Yuan, Haoliang

doi:10.1109/icist49303.2020.9201950

Cited by 4 publications

(1 citation statement)

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“…After analysing and demonstrating the problem of fault classification in transmission lines in detail, we aim to construct a novel model to classify the short-circuit fault by referring to models with good performance used in the previous literatures [18,19]. The short-circuit fault data contain a lot of information, so it is necessary to find the key features.…”

Section: Introductionmentioning

confidence: 99%

Classification of Transmission Line Ground Short Circuit Fault Based on Convolutional Neural Network

Guo¹,

Tang²,

Ao³

et al. 2022

Energy Engineering

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Ground short circuit faults in current transmission lines are common in the power systems. In order to prevent the power system from aggravating the accident caused by short-circuit faults of transmission lines, a novel convolutional neural network (CNN) model is constructed to identify the short-circuit fault of the transmission line in the power system. The CNN model is mainly consisted of five convolutional layers, three max-pooling layers, one concatenate layer, one dropout layer, one fully connected layer, and a Softmax classifier. This method uses a fixed time window to intercept system short-circuit fault data, extracts the deep features of these data from the training samples, and then corresponds the extracted features to labels one-to-one. Finally, in PSCAD/EMTDC, the new England 10 machine 39 nodes are taken as an example to realize the simulation. The experimental results show that the CNN model can quickly and accurately identify the short-circuit fault types, and the optimal model accuracy rate reaches 99.95%. The results of this manuscript -have positive effect on enhancing the disaster prevention capability and the operation stability of transmission lines.

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Section: Introductionmentioning

confidence: 99%