用Transformer-BiGAN实时无监督检测电网异常，精准捕捉微小电压频率波动。
Unsupervised Detection of Spatiotemporal Anomalies in PMU Data Using Transformer-Based BiGAN
- 融合窗口注意力的双向生成对抗网络，建模电网时空依赖关系。
- 在真实硬件测试中达到0.95的ROC-AUC和0.996平均精度。
- 适合电力系统实时监控，无需人工标注故障数据。
保障电网韧性需及时、无监督地检测同步相量数据流中的异常。本文提出T-BiGAN框架，将窗口注意力Transformer融入双向生成对抗网络（BiGAN），通过自注意力编码器-解码器架构捕捉电网复杂的时空依赖性，联合判别器强制循环一致性，使学习到的隐空间与真实数据分布对齐。异常通过结合重构误差、隐空间漂移和判别器置信度的自适应评分实现实时标记。在真实硬件在环的PMU基准上评估，T-BiGAN取得0.95的ROC-AUC和0.996的平均精度，显著优于主流有监督与无监督方法，尤其擅长检测细微的频率与电压偏差，展现出无需人工标注故障数据即可用于实时广域监测的实际价值。
原文摘要 · Abstract (English)
Ensuring power grid resilience requires the timely and unsupervised detection of anomalies in synchrophasor data streams. We introduce T-BiGAN, a novel framework that integrates window-attention Transformers within a bidirectional Generative Adversarial Network (BiGAN) to address this challenge. Its self-attention encoder-decoder architecture captures complex spatio-temporal dependencies across the grid, while a joint discriminator enforces cycle consistency to align the learned latent space with the true data distribution. Anomalies are flagged in real-time using an adaptive score that combines reconstruction error, latent space drift, and discriminator confidence. Evaluated on a realistic hardware-in-the-loop PMU benchmark, T-BiGAN achieves an ROC-AUC of 0.95 and an average precision of 0.996, significantly outperforming leading supervised and unsupervised methods. It shows particular strength in detecting subtle frequency and voltage deviations, demonstrating its practical value for live, wide-area monitoring without relying on manually labeled fault data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。