Fault Diagnosis Method of Power System Based on Neural Network

Xu, Kai

doi:10.1109/icvris.2018.00049

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…However, some samples in classes 11, 12, and 13 were misclassified. For instance, SLG-B (11), SLG-C (12), and DLG-AB are misclassified as DLG-BC ( 14), DLG-AC (15), and DLG-AB (13), respectively, (as shown in Figure 7). This is because transmission line 2 showed similar fault-transient graphs when a fault occurred near the adjacent substation bus (where two generators were installed).…”

Section: Classification Resultsmentioning

confidence: 99%

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Deep-Learning Based Fault Events Analysis in Power Systems

et al. 2022

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The identification of fault types and their locations is crucial for power system protection/operation when a fault occurs in the lines. In general, this involves a human-in-the-loop analysis to capture the transient voltage and current signals using a common format for transient data exchange for power systems (COMTRADE) file. Then, protection engineers can identify the fault types and the line locations after the incident. This paper proposes intelligent and novel methods of faulty line and location detection based on convolutional neural networks in the power system. The three-phase fault information contained in the COMTRADE file is converted to an image file and extracted adaptively by the proposed CNN, which is trained by a large number of images under various kinds of fault conditions and factors. A 500 kV power system is simulated to generate different types of electromagnetic fault transients. The test results show that the proposed CNN-based analyzer can classify the fault types and locations under various conditions and reduce the fault analysis efforts.

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Section: Classification Resultsmentioning

confidence: 99%

“…The authors of [13] showed deep neural networks for power system fault analysis using pattern classification. The input fault features based on an artificial neural network (ANN) have been proposed to identify the location [14].…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning Based Fault Events Analysis in Power Systems

et al. 2022

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“…Based on the existing literature [1][2][3][4][5] , this paper proposes a wavelet neural network fault diagnosis method based on D-PMU information. The adaptive learning rate and momentum combined gradient descent backpropagation algorithm is used to accelerate the convergence speed of the algorithm and improve the fault diagnosis algorithm accuracy.…”

Section: Introductionmentioning

confidence: 99%