基于频谱图的连续记录 IBR 波形中事件的联合检测、定位和分类

中文摘要 AI 辅助

连续记录的高分辨率波形测量提供了有关快速电力系统动态的丰富信息。然而，它们需要自动化方法来识别事件。通过开发基于频谱图的框架来联合检测、定位和分类基于逆变器资源终端处连续记录的实际波形中的事件，解决了这个问题。将此问题重铸为频谱图图像上的时间目标检测问题，因为它们比原始波形数据更明确地捕获瞬态和谐波特征。使用短时傅里叶变换对每个时间序列波形进行变换，并将得到的每个通道的频谱图堆叠成张量用于事件检测。与直接对原始时间序列测量进行操作的检测器进行基准测试。在单相干扰和三相故障上的实验表明，所提出的频谱图方法在事件检测、定位和分类方面始终优于原始波形基线。

英文摘要

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.

发表机构

• University of California, Riverside（加州大学河滨分校）

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