Power outages, whether caused by extreme weather or by local accidents,
require a timely remedy by utility crews.
Repairing distribution infrastructure following an outage could, however, result in the
reconnection of some meters to a transformer or phase other than the one originally
connected. In addition, there are situations,
especially during power emergencies, where
meter connections are rerouted without the
utility’s records ever being updated. Over
time, utility records and physical reality
might diverge sufficiently, making it difficult
to manage the distribution network.
Transformers may fail without warning
because of overloading due to incorrect
connectivity data on record, thus leaving
all connected customers without power.
Meanwhile, other transformers might be
left inadvertently oversized with fewer
meters than the original design specified.
MAPPING TRANSFORMER CONNECTIVITY
Traditionally, verifying transformer and phase connectivity has required
either visual tracing of overhead lines or
sending and receiving electrical signals
over the wire. All traditional methods
require considerable human resources.
Using robust machine learning techniques,
patented algorithms have been developed
to accurately determine meter phase connectivity and physical meter-to-transformer
connectivity using voltage measurements
from customers’ smart meters to determine
an asset’s location.
These algorithms use the normally
occurring voltage fluctuations at each
meter as a signal to identify “friend vs.
stranger,” in a manner of speaking. The
voltage monitored by every meter chang-
es as a consequence of one of three types
• Turning on or off a load at the premise
that the meter monitors. Turning on
an electric oven, for example, causes
a slight decrease in premise voltage.
• Turning on or off a load at a neighboring premise. If an electric oven is
turned on in one premise, the slight
decrease in voltage is felt by all meters
connected to the same transformer.
• Switching a device on the primary side
of the transformer, such as a capacitor
bank switching in or out, or a voltage
regulator adjusting primary voltage.
The meter-to-transformer connectivity discovery method relies on correlating five-minute time series of voltage
changes between any two meters within
Ensuring Efficiency with Accurate Transformer and Phase Connectivity
An Affinity for Proper Connections
This causes unnecessary waste of equipment capacity and power. Theft detection
strategies, based on comparing voltages
of all meters connected to the same
transformer, fail because knowledge of
connectivity is faulty. Similarly, detecting
high impedance connections—a threat to
customer safety—is impaired by the lack
of reliable data on connectivity.
Finally, outage detection and reporting
systems rely on accurate knowledge of
transformer and phase connectivity. In
today’s communication networks, only
a subset of the power-off-notifications
(PONs) sent by all smart meters affected
by an outage are “heard” by the head
end. Precise connectivity information for
each and every transformer is required
to accurately and quickly determine the
true extent and identification of all customers affected by an outage.