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arXiv · 2607.20817

Spectrogram-Based Joint Detection, Localization, and Classification of Events in Continuously Recorded IBR Waveforms

Shivanshu Tripathi·Maziar Raissi·Hamed Mohsenian-Rad
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Abstract

Continuously recorded high-resolution waveform measurements provide rich information about fast power system dynamics. However, they require automated methods to identify events. This problem is addressed by developing a spectrogram-based framework to jointly detect, localize, and classify events in real-world continuously recorded waveforms at the terminal of an Inverter-Based Resource. We recast this problem as a temporal object detection problem on spectrogram images, as they capture the transient and harmonic signatures more explicitly than in raw waveform data. Each time-series waveform is transformed using the short-time Fourier transform, and the resulting per-channel spectrograms are stacked as a tensor for event detection. We benchmark this method against a detector operating directly on raw time-series measurements. Experiments on single-phase disturbances and three-phase faults demonstrate that the proposed spectrogram method consistently improves event detection, localization, and classification over the raw waveform baseline.

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Shivanshu Tripathi, Maziar Raissi, Hamed Mohsenian-Rad. 2026-07-23. Spectrogram-Based Joint Detection, Localization, and Classification of Events in Continuously Recorded IBR Waveforms. https://arxiv.org/abs/2607.20817

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