Research Article
Identification of Anomaly Detection in Power System State Estimation Based on Fuzzy C-Means Algorithm
Table 3
Real time test results of IEEE 30 node power system state estimation and anomaly detection identification.
| Abnormal state | Start time | Identification time of anomaly detection | Delay (s) |
| High temperature (overheating) | 09:23:22 | 09:23:23 | 1 | High temperature (overheating) | 09:23:45 | 09:23:46 | 1 | High temperature (overheating) | 10:02:21 | 10:02:22 | 1 | High temperature (overheating) | 10:11:34 | 10:11:35 | 1 | Low energy discharge | 08:34:23 | 08:34:24 | 1 | Low energy discharge | 09:12:09 | 09:12:10 | 1 | Low energy discharge | 11:34:11 | 11:34:12 | 1 | Low energy discharge | 12:23:09 | 12:23:10 | 1 | High energy discharge | 11:37:11 | 11:37:12 | 1 | Partial discharge | 19:34:23 | 19:34:24 | 1 |
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