用频谱图联合检测定位分类电力系统故障，效果优于原始波形方法。
Spectrogram-Based Joint Detection, Localization, and Classification of Events in Continuously Recorded IBR Waveforms
- 将波形转为频谱图，用图像检测思路处理电力事件
- 在单相扰动和三相故障上均提升检测定位分类精度
- 适合电力系统实时监测与智能诊断场景
连续高分辨率波形数据蕴含丰富的快速电力系统动态信息，但需自动化方法识别事件。本文提出一种基于频谱图的框架，用于在逆变器资源终端的连续波形中联合检测、定位和分类事件。将该问题重构为频谱图上的时序目标检测任务，因其比原始波形更清晰地呈现瞬态和谐波特征。每条时间序列通过短时傅里叶变换生成分通道频谱图，并堆叠成张量用于事件检测。与直接在原始时间序列上运行的检测器相比，该方法在单相扰动和三相故障数据集上的实验表明，提出的频谱图方法在事件检测、定位和分类性能上均持续优于基线。
原文摘要 · Abstract (English)
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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