Event-Type Identification in Power Grids Using a Spectral Correlation Function-Aided Convolutional Neural Network
Rapid and accurate identification of events in power grids is critical to ensuring system reliability and security. This study introduces a novel event-type identification method, utilizing a Spectral Correlation Function (SCF)-aided Convolutional Neural Network (CNN). The proposed method employs a...
Saved in:
| Main Authors: | Ozgur Alaca, Ali Riza Ekti, Jhi-Young Joo, Nils Stenvig |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Open Access Journal of Power and Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10789217/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhancing Power Grid Reliability With Machine Learning and Auxiliary Classifier Generative Adversarial Networks: A Study on Fault Detection Using the Georgia Electric System Load Dataset
by: Hafeez Ur Rehman Siddiqui, et al.
Published: (2025-01-01) -
Phenotyping-based spectral signatures uncover barley cultivars’ sensitivity to combined mildew and drought treatment
by: Chandana Pandey, et al.
Published: (2025-08-01) -
Modeling Puerto Rico Grid’s Sequential Failures for Hurricanes Using Electric Grid Resilience & Assessment System (EGRASS) Tool and Dynamic Contingency Analysis Tool (DCAT)
by: Fernando Bereta Dos Reis, et al.
Published: (2025-01-01) -
Modeling and Detection of Cyber and Physical Attacks on the Control Unit of PV Farm System
by: Aqeel Sajjad Shaeel, et al.
Published: (2025-06-01) -
Power Signal Histograms—A Method of Power Grid Data Compression on the Edge for Real-Time Incipient Fault Forensics
by: Joshua H. Tyler, et al.
Published: (2024-10-01)