Understanding Discrepancy of Power System Dynamic Security Assessment with Unknown Faults: A Reliable Transfer Learning-based Method
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS(2024)
摘要
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 discrep-ancies 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.
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关键词
Adversarial training,dynamic security assessment,maximum classifier discrepancy,missing data,transfer learning
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