<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, facing the digital development of the power grid and the status quo of massive power communication equipment access and targeting the demand for highly intelligent operation and maintenance management of the power grid, combined with neural network technology, we propose an intelligent diagnosis model of power communication equipment faults. Adopting BERT as the vector embedding layer to obtain the vector sequence of fault text, designing a fault entity recognition model for power communication equipment based on BERT-BiGRU-CRF, and completing the construction of the relationship set of fault text. The proposed knowledge graph-based power communication equipment fault intelligent diagnosis model combined with the WBLA-based power communication equipment fault severity level recognition algorithm to obtain different severity fault information, from which a TFIDF-COS-based power communication equipment fault intelligent diagnosis algorithm is designed to realize intelligent diagnosis of power communication equipment faults. After testing, the TFIDF-COS algorithm can get the best optimization effect when the number of hidden layers of the selected algorithm is 1, and the initial learning rate is 0.05, and its accuracy rate can be kept above 98%. Compared with the traditional fault diagnosis system, in terms of the order of magnitude 100M, 500M, 1G, and 5G, the running time is reduced by 322s, 1874s, 4617s, and 7467s, and the accuracy rate is increased by 2.33%, 2.6%, 32.02%, and 61.4%, respectively. Therefore, this paper realizes the accurate positioning of power communication equipment faults and provides technical support for intelligent operation and maintenance of the power grid.</p>
</abstract>

In this paper, facing the digital development of the power grid and the status quo of massive power communication equipment access and targeting the demand for highly intelligent operation and maintenance management of the power grid, combined with neural network technology, we propose an intelligent diagnosis model of power communication equipment faults. Adopting BERT as the vector embedding layer to obtain the vector sequence of fault text, designing a fault entity recognition model for power communication equipment based on BERT-BiGRU-CRF, and completing the construction of the relationship set of fault text. The proposed knowledge graph-based power communication equipment fault intelligent diagnosis model combined with the WBLA-based power communication equipment fault severity level recognition algorithm to obtain different severity fault information, from which a TFIDF-COS-based power communication equipment fault intelligent diagnosis algorithm is designed to realize intelligent diagnosis of power communication equipment faults. After testing, the TFIDF-COS algorithm can get the best optimization effect when the number of hidden layers of the selected algorithm is 1, and the initial learning rate is 0.05, and its accuracy rate can be kept above 98%. Compared with the traditional fault diagnosis system, in terms of the order of magnitude 100M, 500M, 1G, and 5G, the running time is reduced by 322s, 1874s, 4617s, and 7467s, and the accuracy rate is increased by 2.33%, 2.6%, 32.02%, and 61.4%, respectively. Therefore, this paper realizes the accurate positioning of power communication equipment faults and provides technical support for intelligent operation and maintenance of the power grid.

Research on neural network-based fault diagnosis and prediction method for power communication equipment

Nanjing University of Science and Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, facing the digital development of the power grid and the status quo of massive power communication equipment access and targeting the demand for...

Research on neural network-based fault diagnosis and prediction method for power communication equipment

Online veröffentlicht: 13. Dez. 2023

Seitenbereich: -

Eingereicht: 19. Feb. 2023

Akzeptiert: 23. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01457

Schlüsselwörter
BERT-BiGRU-CRF, WBLA algorithm, TFIDF-COS algorithm, Knowledge graph, Equipment fault diagnosis

© 2023 Guang Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on neural network-based fault diagnosis and prediction method for power communication equipment

Online veröffentlicht: 13. Dez. 2023

Seitenbereich: -

Eingereicht: 19. Feb. 2023

Akzeptiert: 23. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01457

SchlüsselwörterBERT-BiGRU-CRF, WBLA algorithm, TFIDF-COS algorithm, Knowledge graph, Equipment fault diagnosis

© 2023 Guang Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
BERT-BiGRU-CRF, WBLA algorithm, TFIDF-COS algorithm, Knowledge graph, Equipment fault diagnosis