- By:
- Dong, Yuqing; Qiu, Wei; Yao, Wenxuan; Yin, He; Liu, Boming ; Dong, Jin ; Kuruganti, Phani Teja V; Liu, Yilu
- Page Number:
- 1-6
- Book Title:
- 2024 IEEE Industry Applications Society Annual Meeting (IAS)
- Publication Date:
- July 2, 2026
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- 2024 IEEE Industry Applications Society Annual Meeting (IAS)
- Conference Location:
- Phoenix, Arizona, United States of America
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/IAS55788.2024.11023778
Abstract
Compared with the information collected from phasor measurement units, synchro-waveforms contain high-fidelity disturbances of the grid, which can be a granular and authentic representation of measurements in the modern power system. However, the dynamic changing morphology makes it challenging to effectively capture various disturbance information from the synchro-waveforms. To tackle this issue, this paper proposes a Synchro-waveform based Temporal Attention (STA) network to achieve rapid event detection. First, a multi-scenario distributed model with renewable integration is established to generate synchro-waveforms under various uncertainties. Then, three typical temporal features are extracted directly from the synchro-waveform measurements. Additionally, the lightweight STA network is deployed to identify the most common event types in renewable energy systems via the self-attention based vision transformer module. The results from simulated experiments demonstrate that the proposed approach can achieve rapid and real-time detection within 0.81 ms and over 96.27 % accuracy.
