Research Article
Identification of Anomaly Detection in Power System State Estimation Based on Fuzzy C-Means Algorithm
Table 2
Real time test results of IEEE 14 node power system state estimation and anomaly detection identification.
| | Abnormal state | Start time | Identification time of anomaly detection | Delay (s) |
| | Medium and low temperature superheat | 13:23:02 | 13:23:03 | 1 | | High temperature (overheating) | 14:34:21 | 14:34:22 | 1 | | High temperature (overheating) | 15:23:22 | 15:23:23 | 1 | | High temperature (overheating) | 15:44:01 | 15:44:02 | 1 | | Low energy discharge | 09:34:02 | 09:34:03 | 1 | | Low energy discharge | 09:45:04 | 09:45:05 | 1 | | Partial discharge | 08:34:02 | 08:34:03 | 1 | | Low energy discharge | 10:01:23 | 10:01:24 | 1 | | High energy discharge | 11:32:23 | 11:32:24 | 1 | | Partial discharge | 09:34:23 | 09:34:24 | 1 |
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