Abstract
Accurate fault detection and classification in power transmission systems are critical to maintaining grid reliability and minimizing outage durations. Traditional methods, often reliant on threshold-based algorithms or manual signal analysis, struggle with dynamic system conditions, high noise interference, and varying fault characteristics. This study proposes a robust framework for fault detection and classification in the IEEE-9 bus system by leveraging image-based feature extraction and machine learning. Phase currents are converted into spectrogram images to capture transient fault signatures in a 2D time-frequency domain. Four statistical metrics—Gini decrease, chi-square, information gain, and information gain ratio—are employed to rank and select high-impact features, reducing dimensionality while preserving discriminative patterns. These features are extracted using SqueezeNet, a lightweight convolutional neural network, and subsequently classified via neural networks and logistic regression. The method is rigorously evaluated under diverse scenarios, including variable fault inception angles (0–360°), high-resistance faults (up to 200 Ω), and white Gaussian noise with signal-to-noise ratios (SNRs) ranging from 10 dB to noise-free (infinite SNR). The results demonstrate 99.5–99.7% precision in distinguishing 10 fault types (LG, LL, LLG, LLLG) and normal operation, with a 99.6–99.8% F1-score at 10 dB SNR, outperforming existing artificial intelligence approaches. The system’s immunity to noise and adaptability to unexpected fault locations underscore its practicality for real-world deployment. By integrating spectrogram visualization with explainable feature selection, this work fills the gap between signal processing and interpretable machine learning, offering a scalable solution for modern power systems.