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Deep Learning Based Fault Detection and Diagnosis Method for Power Systems

Gaoyu Lin

Gaoyu Lin

College of Electrical Engineering and Automation, Fuzhou UniversityFuzhou, China
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Lin, Gaoyu
, 
Huaxiang Zhang

Huaxiang Zhang

College of Electrical Engineering and Automation, Fuzhou UniversityFuzhou, China
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Zhang, Huaxiang
, 
Liangyu Chen

Liangyu Chen

College of Transportation and Civil Engineering of Fujian Agricultural and Forestry UniversityFuzhou, China
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Chen, Liangyu
 und 
Xinyu Chen

Xinyu Chen

College of Physical and Information Engineering, Fuzhou UniversityFuzhou, China
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Chen, Xinyu
  
17. März 2025
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Online veröffentlicht: 17. März 2025
Eingereicht: 11. Okt. 2024
Akzeptiert: 29. Jan. 2025
DOI: https://doi.org/10.2478/amns-2025-0200
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© 2025 Gaoyu Lin et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

LSTM Network structure
LSTM Network structure

Figure 2.

Channel Attention Structure Diagram
Channel Attention Structure Diagram

Figure 3.

The overall structure of the CNN-Attention-LSTM fault diagnosis
The overall structure of the CNN-Attention-LSTM fault diagnosis

Figure 4.

The electrical system fault online diagnosis process
The electrical system fault online diagnosis process

Figure 5.

Test results of failure of power system
Test results of failure of power system

Figure 6.

The three evaluation indicators compared the results
The three evaluation indicators compared the results

Figure 7.

Model validation training results
Model validation training results

Figure 8.

Different prediction model performance evaluation results
Different prediction model performance evaluation results

Figure 9.

The results of the diagnostic results of different methods of the same fault
The results of the diagnostic results of different methods of the same fault
Sprache:
Englisch
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Biologie, Biologie, andere, Mathematik, Angewandte Mathematik, Mathematik, Allgemeines, Physik, Physik, andere
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