POWER November 2020 - 13

O&M
Advantages of AI-Powered
Asset Management for
Utilities
Artificial intelligence (AI) and machine
learning have become major factors in
nearly every industry, and have increasingly
become more popular among utilities.
Automated technology is being used
to inspect offshore wind equipment, and
to improve electricity management and
distribution.
The use of such technologies has a
long list of benefits. It specifically can
provide experienced power operators
with new tools to improve the field connectivity
survey process.
Electric utilities have an obligation to
map and inspect overhead assets to ensure
the electric distribution system is
running safely, and so that the location
of assets can be identified for future inspections.
But the process to conduct
field connectivity surveys is a lengthy
one, traditionally having been done by
operatives walking the electric distribution
network and capturing information
from the assets manually.
In addition to being time-consuming,
other disadvantages of this process include
higher operation and maintenance
(O&M) costs, and a high probability of
capturing inaccurate data. Because the
information is being recorded manually,
it is subject to human error and is
dependent on the skill level, experience,
and diligence of inspectors. After capturing
the data, subject matter experts
review the results and upload them on
a geographic information system (GIS)
manually-which on its own is a timeconsuming
and costly process.
AI and machine learning today are becoming
part of the electric utility toolbox.
By combining " boots on the ground "
operatives and subject matter experts
with a technology that utilizes AI and machine
learning, utilities can create a more
streamlined solution for mapping and inspecting
overhead electric assets.
AI Technology for Asset
Management
While skilled operatives and subject
matter experts hold great value in asset
management, technology can assist the
process by acting as a tool to improve
efficiency and quality of work. One of
the biggest advantages of using an AINovember
2020 | POWER
1. Examples of autonomous identification of assets using ULC Robotics' patent-pending Asset
Identification and Mapping (AIM) system. Courtesy: ULC Robotics
powered technology is that most of the
data collection can be conducted autonomously
at highway speed and processed
in near real-time, which ultimately reduces
manual time spent on surveys, and
contributes to lower O&M costs. Using
this tool also removes the subjectivity
of data and delivers benefits that further
improve system reliability.
In addition to improving the process
overall, the shift to using technology reflects
life in our " new normal " because
it contributes to social distancing in the
field. The use of AI-powered technology
in asset management enables the collection
of data with the fewest possible
onsite operatives, and provides staff with
better tools to complete survey work remotely.
The goal of the solution is not to
replace operators with machines and software,
but to equip them with better tools
to improve efficiency and quality of work.
The concept of an AI-powered asset
management tool already has moved to
the development stage. ULC Robotics developed
a solution called Asset Identification
and Mapping (AIM), which combines
machine learning and a vehicle-mounted
data collection system to inspect the
overhead distribution network.
AIM hardware is composed of highresolution
cameras and a global positioning
system mounted on a ground-based
vehicle. The vehicle is driven along a preidentified
route (Figure 1), while cameras
and other sensors, which are connected
to a computer on the vehicle, collect,
store, and process visual data of overhead
assets using AI.
The data is processed in real-time, or
after the data collection is completed,
and results are uploaded to a secure
cloud-based server for further assesswww.powermag.com
ment
and review. A proprietary machinelearning
algorithm detects target assets,
including poles, transformers, fuses, and
switches, in the video footage collected
by the cameras. An image processing algorithm
also estimates the tilt angle of
the poles and stores this information on
the cloud as metadata for each pole.
Once an asset is correctly identified,
a high-resolution image of the electric
pole and its attachments are stored on
the cloud for further analysis and use.
Through a patent-pending technology, the
software measures the accurate global
position of each asset, which is then used
to autonomously geotag each image.
ULC has also developed a web-based
user interface (Figure 2) that allows staff
to review the inspection results, make
changes, add comments, and generate
reports. In the web-based interface, pole
locations are marked on a GIS mapping
system. The markers on the map are
color-coded based on the type of assets
identified on each pole. Other information
including GPS coordinates, tilt angle,
asset phasing, along with high-resolution
images of the poles and assets, are
stored with each marker for further evaluation
and assessment from the office.
With the COVID-19 pandemic, this past
2. A tablet and cellphone version of the AIM
user interface can be used by crews working
in the field. Courtesy: ULC Robotics
13

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POWER November 2020

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