Invisible units mainly refer to small-scale units that are not monitored by, and thus are not visible to utilities. Integration of these invisible units into power systems does significantly affect the way in which a distribution grid is planned and operated. This paper, based on random matrix theory (RMT), proposes a statistical, data-driven framework to handle the massive grid data, in contrast to its deterministic, model-based counterpart. Combining the RMT-based data-mining framework with conventional techniques, some heuristics are derived as the solution to the invisible units detection and estimation task: linear eigenvalue statistic indicators (LESs) are suggested as the main ingredients of the solution; according to the statistical properties of LESs, the hypothesis testing is formulated to conduct change point detection in the high-dimensional space. The proposed method is promising for anomaly detection and pertinent to current distribution networks-it is capable of detecting invisible power usage and fraudulent behavior while even being able to locate the suspect's location. Case studies, using both simulated data and actual data, validate the proposed method.

翻译：不可见单元主要指未被电力公司监测因而不可见的小规模单元。这些不可见单元接入电力系统会显著影响配电网的规划与运行方式。本文基于随机矩阵理论提出一种统计驱动的数据驱动框架，与传统的确定性模型驱动方法不同，该框架能够处理海量电网数据。通过将基于随机矩阵理论的数据挖掘框架与传统技术相结合，推导出若干启发式方法用于解决不可见单元检测与估计任务：提出线性特征值统计量作为解决方案的主要组成部分；根据线性特征值统计量的统计特性，构建假设检验以在高维空间中进行变点检测。所提方法在异常检测方面具有潜力且适用于当前配电网络——既能检测不可见用电行为与欺诈活动，甚至能定位可疑用户位置。采用仿真数据与实测数据进行的案例研究验证了所提方法的有效性。