@article{Ren2024, 
author = {Chao Ren and Han Yu and Yan Xu and Zhao Yang Dong},
title = {Understanding Discrepancy of Power System Dynamic Security Assessment with Unknown Faults: A Reliable Transfer Learning-based Method},
year = {2024},
journal = {CSEE Journal of Power and Energy Systems},
volume = {10},
number = {1},
pages = {427-431},
keywords = {Adversarial training, dynamic security assessment, maximum classifier discrepancy, missing data, transfer learning},
url = {https://www.sciopen.com/article/10.17775/CSEEJPES.2023.00230},
doi = {10.17775/CSEEJPES.2023.00230},
abstract = {This letter proposes a reliable transfer learning (RTL) method for pre-fault dynamic security assessment (DSA) in power systems to improve DSA performance in the presence of potentially related unknown faults. It takes individual discrepancies into consideration and can handle unknown faults with incomplete data. Extensive experiment results demonstrate high DSA accuracy and computational efficiency of the proposed RTL method. Theoretical analysis shows RTL can guarantee system performance.}
}