A Hierarchical Multimodal AI Framework for Intelligent Alarm Handling in Smart Power Grid Monitoring Systems.
Saved in:
| Title: | A Hierarchical Multimodal AI Framework for Intelligent Alarm Handling in Smart Power Grid Monitoring Systems. |
|---|---|
| Authors: | Li, Tairan1 (AUTHOR), Wang, Qingpeng2 (AUTHOR) 13095011683@163.com, Yang, Gao2 (AUTHOR), Huang, Qiang2 (AUTHOR), Ren, Qingqing2 (AUTHOR), Yang, Hao2 (AUTHOR), Jie, Huamin (AUTHOR) jieh0002@e.ntu.edu.sg |
| Source: | International Transactions on Electrical Energy Systems. 7/11/2026, Vol. 2026, p1-16. 16p. |
| Subject Terms: | *Alarms, *System failures, *Smart power grids, *Long short-term memory, *Machine learning, *Stability criterion, *Detection alarms |
| Abstract: | Recent developments and the rapid expansion of modern power grids have led to alarm flooding. This means that operators receive such a large volume of alarms that they become overwhelmed and stall in inquiry, resulting in mistakes. To better address this issue, an innovative, multifaceted, and hierarchical framework is proposed that combines stability signals and failure event information for intelligent alarm classification and screening. The UCI electrical grid stability and the power system faults datasets, both open source datasets, are used in conjunction with the synthetic multimodal fusion dataset for comprehensive performance evaluation. We benchmark baseline models, including random forest, extreme gradient boosting, and CNN–LSTM, on unimodal datasets. The CNN–LSTM gives us the best temporal stability prediction. At the same time, the XGB has the best heterogeneous fault classification. The proposed hierarchical multimodal model achieves an accuracy of 0.97, an F1 score of 0.96, and an ROC‐AUC of 0.98 for the combination of anomaly detection and hybrid CNN–LSTM–XGB fusion. These results demonstrate that hierarchical multimodal learning provides a scalable, intelligent solution for alarm classification and screening in next‐generation cyberphysical power grid systems. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Recent developments and the rapid expansion of modern power grids have led to alarm flooding. This means that operators receive such a large volume of alarms that they become overwhelmed and stall in inquiry, resulting in mistakes. To better address this issue, an innovative, multifaceted, and hierarchical framework is proposed that combines stability signals and failure event information for intelligent alarm classification and screening. The UCI electrical grid stability and the power system faults datasets, both open source datasets, are used in conjunction with the synthetic multimodal fusion dataset for comprehensive performance evaluation. We benchmark baseline models, including random forest, extreme gradient boosting, and CNN–LSTM, on unimodal datasets. The CNN–LSTM gives us the best temporal stability prediction. At the same time, the XGB has the best heterogeneous fault classification. The proposed hierarchical multimodal model achieves an accuracy of 0.97, an F1 score of 0.96, and an ROC‐AUC of 0.98 for the combination of anomaly detection and hybrid CNN–LSTM–XGB fusion. These results demonstrate that hierarchical multimodal learning provides a scalable, intelligent solution for alarm classification and screening in next‐generation cyberphysical power grid systems. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20507038 |
| DOI: | 10.1155/etep/5528391 |
