Title: Unsupervised Detection of Spatiotemporal Anomalies in PMU Data Using Transformer-Based BiGAN Show Chinese title

Title: 基于Transformer的BiGAN在PMU数据中无监督检测时空异常

Abstract: 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. Show Chinese abstract

Abstract: 确保电网弹性需要及时且无需监督地检测同步相量数据流中的异常。 我们引入了T-BiGAN，一种新颖的框架，它在双向生成对抗网络（BiGAN）中集成了窗口注意力Transformer，以解决这一挑战。 其自注意力编码器-解码器架构捕捉了电网中的复杂时空依赖关系，而联合判别器则通过循环一致性强制将学习到的潜在空间与真实数据分布对齐。 使用结合重构误差、潜在空间漂移和判别器置信度的自适应分数实时标记异常。 在真实的硬件在环PMU基准测试中评估，T-BiGAN实现了0.95的ROC-AUC和0.996的平均精度，显著优于领先的监督和非监督方法。 它在检测细微的频率和电压偏差方面表现出色，展示了其在无需依赖人工标注故障数据的情况下进行实时广域监测的实际价值。