IEEE PES T&D Conference & Exposition 2022 - 87

Proactive functions are essential to detect critical asset
degradation or anomaly conditions before they evolve
into catastrophic failures. This avoidance of costs, damage,
and reliability costs justifies the investment in this
digital transformation.
Data correlation models are time-aligned and trended
among electrical, chemical, and thermal characteristics through
a range of transformer conditions. Reports from the system
(including the energization record, integrated transformer
fault report, and transformer health report) allow capturing
key operational dynamics that provide insight about both
internal and through-fault conditions. Critical transformer
data is compiled into reports and models that are easy to
interpret, supporting informed decision-making. This ultimately
delivers conditions for asset management optimization
and potential for a transformer life extension, enabling
operators to reduce capital and operational expenditures
while maintaining the power system reliability demanded
by today's market.
With advancements in data analytics, interoperable data
communication, and high-speed networking, the availability
of data for advanced analytics is ushering in a new era
of predictive analytics. New predictive analytics can be
applied to build proactive functions which do not just limit
but can prevent catastrophic events. Digital protection relays
are checking for anomalies around every 2 ms. Instead of
witnessing the evolution of equipment failure until the set
threshold of traditional protection function is reached, proactive
functions can provide early warning upon detection of
abnormal operating conditions. These can be key inputs to
managing assets using digital data.
Figure 5 presents an example of a transformer model
from its health report. When a protective relay detects an
internal fault, it trips the transformer. External or through
faults, however, can result in high current flow through the
transformer before the device is tripped offline, causing significant
stress on the transformer. A typical distribution utility
experiences about 15 to 20 through faults each year, even
in advanced distribution systems. Research demonstrates
that many transformers that fail
catastrophically start showing
indications of problems immediately
following a through fault
event. Developing situations such
as these are more easily detected
with visibility into pre- and postfault
electrical, chemical, and
thermal data. In the transformer
relay,
this data is delivered via
the user-friendly integrated transformer
fault report. This graphical
fault report can provide a clear
visual indication of the fault condition
as well as any changes to
the transformer's health.
April 2022 Show Issue
This integrated solution provides a heightened level of
visibility, allowing the data to be applied in a meaningful
way to maintenance or troubleshooting needs. An asset
health report gives a snapshot of the transformer's condition,
and helps utilities analyze the following:
✔ Risk assessment of the transformer population in service,
especially critical transformers and important
connections;
✔ Condition assessment of individual transformers,
identifying those which may require follow-ups such
as visual inspection, diagnostic measurements, and
maintenance; and
✔ Proactive risk mitigation actions for transformer asset
management.
An Example of Grid Utilization
and Fault Location
The integration of renewable generation into existing grids
is increasing. One of the main challenges is that power flows
are uneven and decentralized, and line loading at specific
points in the network becomes uncertain with the change in
the feed. This challenge is reduced with real-time or neartime
grid visibility. Using dynamic line rating data monitored
by sensors, the power handling capacity of the line at
specific points can be determined. This allows the utility to
provide improved power quality and a better-managed system
with increased visibility into the network. With a regulatory
requirement to minimize outage times, it is important
to detect and locate faults and disturbances across the distribution
grid using overhead line monitoring devices. The
integration of monitoring data received from line monitoring
devices with a distribution management system helps
improve the reliability and safety of the system, and thus
optimizes the use of the network. Typically, line monitoring
devices are mounted at strategic points in the overhead
network. The system is shown in Figure 6.
These line sensors measure current and calculate both
amplitude and phase of the root-mean-square value, and
have built-in communications. The sensors can pick up fault
figure 5. An example of holistic transformer health models.
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87
IEEE PES T&D Conference & Exposition 2022

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