论文标题
基于学习的实时事件使用丰富的真实PMU数据
Learning-Based Real-Time Event Identification Using Rich Real PMU Data
论文作者
论文摘要
从数据角度来看，大规模的相量测量单元（PMU）揭示了电力系统固有的物理定律，从而增强了对电源系统操作的认识。但是，PMU时间序列和不完美数据质量的高粒度和非平稳性可能会给实时系统事件识别带来巨大的技术挑战。为了解决这些问题，本文提出了一个基于两阶段的学习框架。在第一阶段，利用马尔可夫过渡场（MTF）算法来提取潜在数据特征，通过编码图中PMU数据的时间依赖性和过渡统计。然后，建立了空间金字塔（SPP）卷积神经网络（CNN），以有效，准确地识别操作事件。所提出的方法完全构建，并在位于美国各地的几十个PMU源（以及相应的事件日志）的大型真实数据集上进行了测试，连续两年跨度。数值结果验证了我们的方法具有很高的识别精度，同时显示出良好的数据质量质量。
A large-scale deployment of phasor measurement units (PMUs) that reveal the inherent physical laws of power systems from a data perspective enables an enhanced awareness of power system operation. However, the high-granularity and non-stationary nature of PMU time series and imperfect data quality could bring great technical challenges to real-time system event identification. To address these issues, this paper proposes a two-stage learning-based framework. At the first stage, a Markov transition field (MTF) algorithm is exploited to extract the latent data features by encoding temporal dependency and transition statistics of PMU data in graphs. Then, a spatial pyramid pooling (SPP)-aided convolutional neural network (CNN) is established to efficiently and accurately identify operation events. The proposed method fully builds on and is also tested on a large real dataset from several tens of PMU sources (and the corresponding event logs), located across the U.S., with a time span of two consecutive years. The numerical results validate that our method has high identification accuracy while showing good robustness against poor data quality.
