  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.
