Operational disturbance monitoring in power networks requires decisions to be made from waveform windows as they arrive, rather than from completed records after the event. This study evaluates full-vector Wigner--Ville Distribution Slice (WVDS) spectra for sequential anomaly-onset detection in high-voltage grid-voltage waveforms. The approach keeps the bilinear midpoint interaction structure of the Wigner--Ville distribution and represents each 128-sample voltage window by a 128-dimensional slice spectrum, avoiding manually selected fault-frequency markers. WVDS is used with a baseline-normalized deviation (BND) score and is compared against the BND of Fast Fourier Transform (FFT-BND), raw-window autoencoders, FFT autoencoders, and WVDS autoencoders under the same thresholding and three-window persistence rule. A synthetic autoencoder--clustering teacher is used to select RTE fault records that start from an initially normal region and then transition to anomalous behavior. On the filtered test set, FFT-BND achieves the highest sensitivity, whereas WVDS-BND provides the lowest false-alarm operating point, reducing record-level pre-onset false alarms to 0.69%. The autoencoder comparison follows the same selectivity pattern: WVDS reconstruction decreases false alarms relative to FFT reconstruction but misses more examples. The results indicate that preserved WVD cross-term information can form a selective representation for online grid-waveform anomaly monitoring when false alarms are costly.

翻译：电力网络运行扰动监测需要在波形窗口到达时做出决策，而非事件发生后基于完整记录进行分析。本研究评估了全矢量维格纳-维利分布切片（WVDS）谱在高压电网电压波形中用于序列异常起始检测的效果。该方法保留维格纳-维利分布的双线性中点交互结构，将每个128采样点的电压窗口表示为128维切片谱，从而避免人工选择故障频率标记。WVDS与基线归一化偏差（BND）得分结合使用，并在相同阈值及三窗口持久性规则下，与快速傅里叶变换的BND（FFT-BND）、原始窗口自编码器、FFT自编码器及WVDS自编码器进行对比。采用合成自编码器-聚类教师模型从法国输电网（RTE）故障记录中筛选出初始处于正常区域、随后转变为异常行为的数据。在过滤后的测试集上，FFT-BND实现了最高灵敏度，而WVDS-BND提供了最低虚警操作点，将记录级起始前虚警率降至0.69%。自编码器对比遵循相同的选择性模式：相较于FFT重构，WVDS重构降低了虚警，但漏检了更多实例。结果表明，在虚警代价高昂的情况下，保留维格纳-维利分布交叉项信息可形成面向电网波形在线异常监测的选择性表示。