Power System Transient Stability Assessment Using Convolutional Neural Network and Saliency Map

• Published in: Energies (Basel) Vol. 16; no. 23; p. 7743
• Main Authors: Lee, Heungseok, Kim, Jongju, Park, June Ho, Chung, Sang-Hwa
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-12-2023
• Subjects: Accuracy; Artificial intelligence; Classification; convolutional neural network; deep learning; Electric power systems; Forecasts and trends; Neural networks; saliency map; Simulation methods; South Korea; transient stability assessment; South Korea
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16237743

This study proposes a model for transient stability assessment, which is a convolutional neural network model combined with a saliency map (S–CNN model). The convolutional neural network model is trained on dynamic data acquired through the data measurement devices of a power system. Applying the saliency map to the acquired dynamic data visually highlights the critical aspects of transient stability assessment. This reduces data training time by eliminating unnecessary aspects during the convolutional neural network model training, thus improving training efficiency. As a result, the proposed model can achieve high performance in transient stability assessment. The dynamic data are acquired by configuring benchmark models, IEEE 39 and 118 bus systems, through MATLAB/Simulink and performing time-domain simulations. Based on the acquired dynamic data, the performance of the proposed model is verified through a confusion matrix. Furthermore, an analysis of the effects of noise interference on the performance is conducted.