Investigating the Performance of MLE and CNN for Transient Stability Assessment in Power Systems

Abstract

In power systems, maintaining transient stability is crucial to avoid unanticipated blackouts. The role of Transient Stability Assessment (TSA) is vital for quickly identifying and promptly addressing instabilities. TSA facilitates rapid reactions to serious fault conditions. This paper pioneers the integrated comparison of two distinct methodologies—Maximal Lyapunov Exponent (MLE) methods and Convolutional Neural Networks (CNN)—in a single unified framework for transient stability assessment in power systems, uniquely evaluating their accuracy and reliability for TSA. The CNN-based method uses measured time series data from voltage magnitude, phase angle, and frequency measurements at generator buses, while the MLE approach utilizes both phase angles and frequency of generator buses. This paper provides a qualitative and quantitative comparison of the performance and accuracy of MLE and CNN. This research utilizes the same case studies conducted on the Nordic32 system for both MLE and CNN to ensure robust, unbiased comparisons and promote interdisciplinary research, aligning with current trends in AI integration in power systems.

Keywords

• Maximal Lyapunov exponent (MLE)
• convolutional neural networks (CNN)
• time domain simulation (TDS)
• transient stability assessment (TSA)
• phasor measurement unit (PMU)