论文标题
基于无监督时间序列数据分析的成本效益不良同步数据检测
Cost-Effective Bad Synchrophasor Data Detection Based on Unsupervised Time Series Data Analytics
论文作者
论文摘要
在具有各种高级传感器的现代智能电网中，例如，相量测量单元（PMU），在实践中，差异（异常）测量始终是不可避免的。考虑到要滤除潜在不良数据的势在必行，本文通过充分探索空间 - 暂时性相关性，开发了一种新型的在线糟糕的PMU数据检测（BPDD）方法，用于区域相量数据集中器（PDC）。由于不需要昂贵的数据标记或迭代学习，它可以从空间 - 周期性的邻居（STNN）发现的新数据驱动的角度执行无模型，无标记和非题量BPDD。具体而言，PMU所获得的空间相关的区域测量首先被收集为时空时间序列（TS）。之后，通过表征异常的STNN来识别被不良PMU数据污染的TS子序列。为了使整个方法能够处理在线流媒体PMU数据，精心设计了加速STNN发现的有效策略。与现有数据驱动的BPDD解决方案不同，需要昂贵的离线数据集准备/培训或计算密集的在线优化，它可以以高度成本有效的方式实施，从而在实际情况下更适用和可扩展。北欧测试系统和现实的中国南部电网的数值测试结果证明了在实用在线监控中提出的方法的可靠性，效率和可扩展性。
In modern smart grids deployed with various advanced sensors, e.g., phasor measurement units (PMUs), bad (anomalous) measurements are always inevitable in practice. Considering the imperative need for filtering out potential bad data, this paper develops a novel online bad PMU data detection (BPDD) approach for regional phasor data concentrators (PDCs) by sufficiently exploring spatial-temporal correlations. With no need for costly data labeling or iterative learning, it performs model-free, label-free, and non-iterative BPDD in power grids from a new data-driven perspective of spatial-temporal nearest neighbor (STNN) discovery. Specifically, spatial-temporally correlated regional measurements acquired by PMUs are first gathered as a spatial-temporal time series (TS) profile. Afterwards, TS subsequences contaminated with bad PMU data are identified by characterizing anomalous STNNs. To make the whole approach competent in processing online streaming PMU data, an efficient strategy for accelerating STNN discovery is carefully designed. Different from existing data-driven BPDD solutions requiring either costly offline dataset preparation/training or computationally intensive online optimization, it can be implemented in a highly cost-effective way, thereby being more applicable and scalable in practical contexts. Numerical test results on the Nordic test system and the realistic China Southern Power Grid demonstrate the reliability, efficiency and scalability of the proposed approach in practical online monitoring.
