| Title |
A Study on Power System Transient Stability Assessment Using Deep Learning |
| Authors |
이흥석(Heungseok Lee) ; 김종주(Jongju Kim) ; 박준호(June Ho Park) ; 정상화(Sang-Hwa Chung) |
| DOI |
https://doi.org/10.5370/KIEE.2023.72.11.1340 |
| Keywords |
Tranisent Stability Assessment; Deep Learning; Saliency Map; Convolution Neural Network |
| Abstract |
This paper proposes a deep learning based CNN(Convolutional Neural Network) model combining Saliency map for the transient stability assessment of power systems. The CNN model is learned using data obtained from PMU(Phasor Measurement Units) which are high-speed sampling devices to allowing us precisely grasp the dynamic characteristics of the power system. The use of Saliency map enables the visual representation of the most influential input features in the CNN model. The proposed model shows more accurate and rapid transient stability assessment of power systems. The performance of the proposed model is verified using simulation data obtained from the IEEE 39 bus system through MATLAB/Simulink. |