Abstract
With the widespread use of power electronic equipment, accurately identifying soft faults in power electronic converters is crucial for the stable and reliable operation of power electronic equipment. This paper proposed a soft fault diagnosis method for three-phase DC/AC converter using a data-based approach. Firstly, simulation analysis is conducted on the soft faults of three-phase DCI AC converter, and appropriate test signals are selected as data sources. Then, the LSTM neural network and its variants are used to obtain fault diagnosis models. Finally, the method is validated through experiments, and the results show that it has good performance in soft fault diagnosis of the three-phase DCI AC converter.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2024 IEEE 7th International Electrical and Energy Conference, CIEEC 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 4722-4727 |
| Number of pages | 6 |
| ISBN (Electronic) | 9798350359558 |
| DOIs | |
| State | Published - 2024 |
| Event | 7th IEEE International Electrical and Energy Conference, CIEEC 2024 - Harbin, China Duration: 10 May 2024 → 12 May 2024 |
Publication series
| Name | Proceedings of 2024 IEEE 7th International Electrical and Energy Conference, CIEEC 2024 |
|---|
Conference
| Conference | 7th IEEE International Electrical and Energy Conference, CIEEC 2024 |
|---|---|
| Country/Territory | China |
| City | Harbin |
| Period | 10/05/24 → 12/05/24 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- LSTM neural network
- fault diagnosis
- soft fault
- three-phase DCIAC converter
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