pwr_february-2024 - 7

ARTIFICIAL INTELLIGENCE
2. Researchers from Switzerland, who set out to understand where interest is burgeoning in
AI applications, found that global AI research trends for the power sector focused keenly on
" learning, " followed by " planning. " Courtesy: F. Heymann, H. Quest, T. Lopez Garcia, C. Ballif,
M. Galus, Reviewing 40 years of artificial intelligence applied to power systems-A taxonomic
perspective, Energy and AI, 2024.
anomalies, exploiting advances in deep
learning. Other applications center on
forecasting, which is used for unit commitment,
generation scheduling, economic
dispatch, reserve estimation, and
power system reliability.
Predictive maintenance appears to
be emerging as a burgeoning focus.
" AI tools can parse through substantial
amounts of data and potentially identify
early-stage trends in data that may
lead to identification of future malfunctions
and notify power plant and grid
operators to schedule preventive maintenance
ahead of these potential equipment
failures, " Renshaw noted. EPRI
has already applied such principles to
wind turbine gearboxes to predict the
onset of failures such that lower-cost
preventive maintenance activities can
be performed instead of larger-scale and
more expensive repairs.
In just one of the hundreds of examples
sector-wide, American Bituminous
Power's (AMBIT's) 80-MW Grant Town
Power Project-West Virginia's only remaining
coal refuse-fired facility-has
partnered with robotics firm Gecko Robotics
to leverage data-collecting robots
(Figure 3), data integration, analytic
tools, and software modules to " give
plant managers incredible clarity, " Gecko
Robotics co-founder and CEO Jake Loosararian
told POWER. " There is no more
guesswork-we can predict exactly
what will fail, automate repair pans using
AI to maximize budgets and increase the
useful life of customer infrastructure. "
Asset optimization-efforts to maximize
asset efficiency and reliability to
balance performance with maintenance
needs-is another major focus area. Stacey
Jones, ABB's global portfolio leader
for Asset Performance Management
(APM) solutions, told POWER ABB's
APM system is layered with AI and ML.
" The first layer is more condition monitoring,
which is simple threshold monitoring,
which is not AI, and then some
first principles models. Then, for our next
layer of advanced protection, we have
what we call our advanced prediction
software-APM 360, " she explained.
" This is where we start to bring in the
AI in the ML " to essentially use the data
" to train a model that's looking for certain
behavior or a set of characteristics,
and anytime we diverge outside of that,
then we're going to throw out an alert,
and ideally, you catch it. "
AI for Transmission and
Distribution
3. Grant Town, a coal refuse plant in West
Virginia, deployed Cantilever, Gecko's end-toend
industrial asset management solution, to
pinpoint damaged boiler tubes. Gecko says
the robotic inspection-assisted analysis generated
28 million data points in an eight-hour
data turnaround. Courtesy: Gecko
February 2024 | POWER
AI applications in transmission networks,
meanwhile, currently revolve heavily
around transmission system expansion
planning. Approaches so far have focused
on sizing and siting transmission
assets using heuristic and metaheuristic
optimization techniques. However, applications
with learning attributes are also
burgeoning on the operations side, for
example, to bolster fault protection or
dynamic line rating. Splight, an AI grid
management firm, suggested these
www.powermag.com
prospects for AI are slated to thrive given
the Federal Energy Regulatory Commission's
Order 881, which requires a transition
from traditional static to the dynamic
concept of ambient-adjusted ratings
(AARs) for transmission lines.
" In the context of the power grid, AI
algorithms are poised to revolutionize
how we handle temperature data and
make real-time adjustments to line ratings,
providing an elegant solution to
the intricate challenge of implementing
dynamic line rating, " the company said.
AI's potential lies in real-time line rating
adjustments and optimizing grid capacity
utilization, which, in turn, improves
efficiency while allowing the seamless
integration of renewables. In addition, AI
enables cost savings, enhances safety,
and because it provides accurate load
forecasting, it could bolster data-driven
decision-making that caters to decarbonization,
it said.
Distribution networks, too, are exploiting
learning and planning, using various
types of optimization algorithms for line
routing and substation placement. AI is
also assisting in automated fault location
in distribution systems, stability analysis,
and automated control. AI-based controllers
are also well-suited for microgrid environments,
though microgrid modeling
also exploits the ability to compensate
for non-complete or inexact models, and
reinforcement learning.
Researchers at the University of California,
Santa Cruz in November notably unveiled
a " specific flavor " or reinforcement
learning-constrained policy optimization
(CPO)-to optimize how microgrids pull
from various alternate sources of power.
Unlike traditional systems using model
predictive control (MPC), which bases decisions
simply on the available conditions
at the time of optimization, the CPO approach
" takes into account real-time conditions
and uses machine learning to find
long-term patterns that affect the output of
renewables, such as the varying demand
on the grid at a given time and intermittent
weather factors that affect renewable
sources, " the university explained.
Broader Applications for AI
Sector-wide
On the business front, AI applications
are thriving in market operations and
trading, such as for price forecasting, using
probabilistic time series forecasting,
and optimized aggregation and bidding
of flexible demand. AI applications in
the power retail business are meanwhile
helping to predict electricity demand and
7

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pwr_february-2024

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