Research on real-time data processing and evaluation of new power system wide-area digital metering equipment based on deep learning algorithm
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24 mar 2025
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Publicado en línea: 24 mar 2025
Recibido: 05 nov 2024
Aceptado: 24 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0798
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© 2025 Dongsheng Xue et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Configure WRED parameters for integrated transmission of power information
| Information type | Queue threshold Minimum threshold | Queue threshold Indicates the maximum threshold | Maximum discard probability |
|---|---|---|---|
| Emergency control and protection | 88.65% | 100.00% | 0.173 |
| Wide area surveillance | 76.29% | 100.00% | 0.298 |
| SCADA | 76.29% | 100.00% | 0.611 |
| Fault information | 59.23% | 90.00% | 0.897 |
| Management information | 54.37% | 90.00% | 0.992 |
| Multimedia information | 49.67% | 90.00% | 0.997 |
The root-mean-square error of the test set varies with response time
| Response time (s) | Stability evaluation model MSE | Instability degree evaluation model MSE |
|---|---|---|
| 0.0138 | 0.0056 | 0.0176 |
| 0.1 | 0.0045 | 0.0146 |
| 0.2 | 0.0038 | 0.0076 |
| 0.3 | 0.0026 | 0.0039 |
| 0.4 | 0.0019 | 0.0037 |
| 0.5 | 0.0018 | 0.0036 |
| 0.6 | 0.0016 | 0.0018 |
| 0.7 | 0.0014 | 0.0016 |
| 0.8 | 0.0012 | 0.0014 |
| 0.9 | 0.0011 | 0.0011 |
| 1.0 | 0.0011 | 0.0011 |