论文标题
对电力系统进行高分辨率监测的异常序列检测的深度学习方法
A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems
论文作者
论文摘要
提出了一种深度学习方法，以使用高分辨率连续波（CPOW）或相组测量值来检测数据和系统异常。假定无异常和无异常测量模型具有未知的时间依赖性和概率分布。假定没有异常模型的历史训练样本，而没有用于异常测量的训练样本。通过将无异常观测转换为通过生成对抗网络的独立且分布相同的序列，提出的方法在传感器级别部署了对异常检测的均匀性测试。还提出了结合控制中心的传感器级检测的分布式检测方案，该方案还提出了结合局部检测以形成更可靠的检测的。数值结果表明，使用真实和合成CPOW和PMU数据集，针对各种坏数据案例的最先进解决方案有了显着改善。
A deep learning approach is proposed to detect data and system anomalies using high-resolution continuous point-on-wave (CPOW) or phasor measurements. Both the anomaly and anomaly-free measurement models are assumed to have unknown temporal dependencies and probability distributions. Historical training samples are assumed for the anomaly-free model, while no training samples are available for the anomaly measurements. By transforming the anomaly-free observations into uniform independent and identically distributed sequences via a generative adversarial network, the proposed approach deploys a uniformity test for anomaly detection at the sensor level. A distributed detection scheme that combines sensor level detections at the control center is also proposed that combines local detections to form more reliable detections. Numerical results demonstrate significant improvement over the state-of-the-art solutions for various bad-data cases using real and synthetic CPOW and PMU data sets.
