학술논문
Empirical Study of Model Repair of DNN for Power System Transient Stability Assessment
Document Type
Conference
Author
Source
2023 IEEE 7th Conference on Energy Internet and Energy System Integration (EI2) Energy Internet and Energy System Integration (EI2), 2023 IEEE 7th Conference on. :1-5 Dec, 2023
Subject
Language
Abstract
Deep Neural Network (DNN) has been introduced to power system transient stability assessment (TSA) and achieved quite remarkable success. However, since the DNNs are ‘blackbox’ models, it is very hard to reveal misbehaviors of the DNNs and repair them to get better performance. To address these challenges, this paper proposes to use Arachne, a fresh program repair technique for DNNs, to repair TSA DNNs. Arachne can fix DNN's specific misclassifications at little cost to overall accuracy. In TSA, missed detections is more severe to the system than false alarms due to the conservation requirements. Case studies have demonstrated the usefulness of the proposed method.