  With more distributed energy resources (DERs) connected to distribution
grids, better monitoring and control are needed, where identifying the topology
accurately is the prerequisite. However, due to frequent re-configurations,
operators usually cannot know a complete structure in distribution grids.
Luckily, the growing data from smart sensors, restricted by Ohm law, provides
the possibility of topology inference. In this paper, we show how line
parameters of Ohm equation can be estimated for topology identification even
when there are hidden nodes. Specifically, the introduced learning method
recursively conducts hidden-node detection and impedance calculation. However,
the assumptions on uncorrelated data, availability of phasor measurements, and
a balanced system, are not met in practices, causing large errors. To resolve
these problems, we employ Cholesky whitening first with a proof for measurement
decorrelations. For increasing robustness further, we show how to handle
practical scenarios when only measurement magnitudes are available or when the
grid is three-phase unbalanced. Numerical performance is verified on multi-size
distribution grids with both simulation and real-world data.
