Transient Stability Assessment Using Deep Transfer Learning

• Published in: IEEE access 2023, Vol.11, p.116622-116637
• Main Authors: Kim, Jongju, Lee, Heungseok, Kim, Sungshin, Chung, Sang-Hwa, Park, June Ho
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Convolutional neural networks; Deep learning; deep transfer learning; Feature maps; Freezing; ImageNet; Learning; Measuring instruments; Neural networks; Phasors; Power system stability; pre-trained deep convolutional neural network; Stability analysis; Stability criteria; Training; Transfer learning; Transient analysis; Transient stability; Transient stability assessment; VGG
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3320051

This study proposes a deep transfer learning method using a deep convolutional neural network pre-trained with ImageNet for transient stability assessment. The procedure of deep transfer learning, incorporating the role and considerations of the transient stability assessment system, is suggested. The transient assessment system learns the relationship between the severity of disturbances and transient stability in the power system through the proposed training method. The severity of a disturbance based on the physical causal relationship of the angle stability is proposed to train and implement the transient stability assessment model. The power system state variables obtained from the phasor measurement unit are converted into the feature map described by the severity of a disturbance, enabling the training of transient stability characteristics of the power system to the deep convolutional neural network. The training dataset is constructed using the time-domain simulation on IEEE 39 and IEEE 118-bus benchmark power system models configured in MATLAB/Simulink. As a result of deep transfer learning, which involves training with freezing some of the convolutional layers of the pre-trained deep network, the most suitable model for transient stability assessment is selected among the pre-trained deep networks. The effectiveness of the proposed method is compared with other approaches using the confusion matrix, and the robustness against noise interference is also investigated.