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.

中文翻译：

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基于随机矩阵理论的隐形单位检测与估计

隐形单位主要是指不受公用事业监控，因此对公用事业单位不可见的小型单位。将这些隐形单元集成到电力系统中确实会显着影响配电网的规划和运营方式。本文基于随机矩阵理论 (RMT)，提出了一个统计的、数据驱动的框架来处理大量网格数据，而不是其确定性的、基于模型的对应物。将基于 RMT 的数据挖掘框架与传统技术相结合，推导出一些启发式方法作为隐形单元检测和估计任务的解决方案：建议线性特征值统计指标 (LES) 作为解决方案的主要成分；根据 LES 的统计特性，假设检验是为了在高维空间中进行变化点检测而制定的。所提出的方法有望用于异常检测并与当前的配电网络相关——它能够检测隐形电力使用和欺诈行为，甚至能够定位嫌疑人的位置。使用模拟数据和实际数据的案例研究验证了所提出的方法。