Power Consumption Profiling Using Energy Time-Frequency Distributions in Smart Grids
Power Consumption Profiling Using Energy Time-Frequency Distributions in Smart Grids
复制标题
DOI：
10.1109/lcomm.2014.2371035
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Angelos K. Marnerides;Paul Smith;A. E. S. Filho;A. Mauthe
中科院分区：
文献类型：
--
作者：
Angelos K. Marnerides;Paul Smith;A. E. S. Filho;A. Mauthe
智能电网是包括重要通信基础设施的配电网络，用于收集使用数据和监控电网的运行状态。由于这些额外的基础设施，电力网络面临着更大的网络攻击风险。在这封信中，我们讨论了在智能电网的亚米级功耗测量中发生的异常情况的检测和归类问题，这些异常可能表明存在恶意行为。我们通过对一组功率测量的统计特征进行聚类来实现这一点，这些统计特征是使用平滑的伪Wigner Ville(SPWV)能量时间-频率(TF)分布确定的。我们展示了这种方法如何能够比简单地使用原始功率测量更准确地区分能源消耗集群。我们的最终目标是将分析功耗测量的原则作为增强的智能电网异常检测系统的一部分。
Smart grids are power distribution networks that include a significant communication infrastructure, which is used to collect usage data and monitor the operational status of the grid. As a consequence of this additional infrastructure, power networks are at an increased risk of cyber-attacks. In this letter, we address the problem of detecting and attributing anomalies that occur in the sub-meter power consumption measurements of a smart grid, which could be indicative of malicious behavior. We achieve this by clustering a set of statistical features of power measurements that are determined using the Smoothed Pseudo Wigner Ville (SPWV) energy Time-Frequency (TF) distribution. We show how this approach is able to more accurately distinguish clusters of energy consumption than simply using raw power measurements. Our ultimate goal is to apply the principles of profiling power consumption measurements as part of an enhanced anomaly detection system for smart grids.