Evaluating Time-Frequency Representations for Intelligent Fault Analysis in Power System Protection

Abstract

This project investigates how different time-frequency representations, including spectrograms, wavelets, scalograms, and Gramian Angular Fields, affect deep learning performance in fault analysis tasks. Using high-resolution current and voltage signals, it benchmarks various representation strategies across multiple neural network architectures. The goal is to determine which transformations capture fault dynamics most effectively and enable robust model generalization. The findings will guide the design of future machine learning based protection systems for resilient and data-driven power grids.