Power System Event Identification with Transfer Learning Using Large-scale Real-world Synchrophasor Data in the United States
- University of California,Department of Electrical and Computer Engineering,Riverside,CA,92521
The lack of sufficient labeled events and long training time limit the applicability of deep neural network-based power system event identification using synchrophasor data. In this paper, we propose to leverage transfer learning technique to boost the reliability and reduce the required training time of neural classifier for power system event identification. We use the weights of a neural classifier trained on one transmission system as the initial parameters of another neural classifier for a different transmission system. Numerical tests with real-world synchrophasor data from the Eastern and Western Interconnections of the United States show that the proposed transfer learning approach is very effective in not only improving the training reliability but also reducing the training time.
- Research Organization:
- Univ. of California, Riverside, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- OE0000916
- OSTI ID:
- 1958413
- Report Number(s):
- DOE-UCR-2022-ISGT-NA
- Journal Information:
- 2022 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Conference: 2022 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), New Orleans, LA, USA, 24-28 April 2022
- Country of Publication:
- United States
- Language:
- English
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