Transient Stability Assessment Using Deep Transfer Learning

被引:7
|
作者
Kim, Jongju [1 ]
Lee, Heungseok [2 ]
Kim, Sungshin [2 ]
Chung, Sang-Hwa [3 ,4 ]
Park, June Ho [2 ,4 ]
机构
[1] Korea Southern Power Co Ltd, Busan 48400, South Korea
[2] Pusan Natl Univ, Dept Elect Engn, Busan 46241, South Korea
[3] Pusan Natl Univ, Dept Informat Convergence Engn, Busan 46241, South Korea
[4] Dong Nam Grand ICT Res & Dev Ctr, Busan 48059, South Korea
基金
新加坡国家研究基金会;
关键词
Power system stability; Stability criteria; Transient analysis; Convolutional neural networks; Neural networks; Training; Transfer learning; Deep learning; Transient stability assessment; deep transfer learning; pre-trained deep convolutional neural network; VGG; power system stability; deep learning; ImageNet; POWER-SYSTEM; MODEL; PREDICTION; MACHINE; NETWORK; ENERGY;
D O I
10.1109/ACCESS.2023.3320051
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:116622 / 116637
页数:16
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