  The present distribution grids generally have limited sensing capabilities
and are therefore characterized by low observability. Improved observability is
a prerequisite for increasing the hosting capacity of distributed energy
resources such as solar photovoltaics (PV) in distribution grids. In this
context, this paper presents learning-aided low-voltage estimation using
untapped but readily available and widely distributed sensors from cable
television (CATV) networks. The CATV sensors offer timely local voltage
magnitude sensing with 5-minute resolution and can provide an order of
magnitude more data on the state of a distribution system than currently
deployed utility sensors. The proposed solution incorporates voltage readings
from neighboring CATV sensors, taking into account spatio-temporal aspects of
the observations, and estimates single-phase voltage magnitudes at all
non-monitored buses using random forest. The effectiveness of the proposed
approach was demonstrated using a 1572-bus feeder from the SMART-DS data set
for two case studies - passive distribution feeder (without PV) and active
distribution feeder (with PV). The analysis was conducted on simulated data,
and the results show voltage estimates with a high degree of accuracy, even at
extremely low percentages of observable nodes.
