POWER February 2020 - 23

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networks. ML, however, is better understood
by the two tasks that define it: supervised
and unsupervised learning.
In supervised learning, " the learning is
done using a ground truth, where there
is prior knowledge of what the output
values for a sample data set should be, "
it says. Essentially, its goal is to " learn a
function that best approximates the relationship
between a data sample and the
desired outputs, " and it then uses that
function to predict the output for a given
input data set. " Supervised learning is
mainly used for classification problems;
common algorithms include regression,
support vector machines, artificial neural
networks (ANNs), and random forests. "
This makes it well-suited for power sector
applications such as asset management,
which could use a set of images
to recognize damage, for example.
Unsupervised learning, meanwhile,
does not required labeled data, and its
key goal is to learn an inherent structure
within a set of data points. " Unsupervised
learning is mainly used for clustering
problems, " EPRI explains. So, for
example, unsupervised learning could
accelerate the analysis of images of
damaged assets by clustering similar images,
allowing a human to label just one
image per category.
Natural Language Processing (NLP).
NLP helps computers capture, interpret,
and make sense of human languages
using supervised or unsupervised learning
methods. Drawing from the fields of
computer science and linguistics, it is
used, for example, for text analysis and
creating chatbots. " One industry-specific
application of NLP is the processing of
documents such as injury reports to analyze
and make recommendations for safe
operations, " EPRI says.
Artificial Neural Networks (ANNs).
Inspired by the human brain, ANNs are
essentially input and output layers as
well as a hidden layer that transforms the
input so that it is usable by the output
layer. An ANN with multiple hidden layers
is known as a " deep neural network. "
Deep neural networks learn from a large
amount of data, using algorithms to perform
a task repeatedly, each time adjusting
it to improve outcomes-almost in
the way a human learns from experience.
The process is more commonly known
as " deep learning, " another buzzword
gaining traction in the industry. " Image
recognition for identifying damaged assets
and predictive maintenance applications
for assets are examples of ANNs
and deep learning algorithms applied in
February 2020 | POWER
the power industry, " EPRI says.
AutoML. EPRI describes this as the
" end-to-end process of automated application
of machine learning to real-world
problems, such as data preparation, feature
engineering, and model selection. "
It notes this is gaining attention " due to
the lack of expertise in applying machine
learning techniques. "
A Vast Potential
Every expert POWER spoke to lauded
AI's future potential. Most agreed that
its ability to leverage operational value as
the sector grapples with multiple disruptions-including
decarbonization, decentralization,
and digitization-is perhaps
its biggest promise.
According to Upadhye, based on
EPRI's work as well as on interest industry
has expressed, AI tools available
today offer the best potential to resolve
issues using imaging, for example, as an
application to relieve the time-consuming
and tedious inspections process. " Pretty
much every asset in the utility space
gets inspected one way or the other, and
the amount of actions that can be automated
using AI are immense, " he said. It
also appears that AI's potential will likely
extend to applications that involve assets
with sensors that generate time series-
type data. " Analysis of that sort of data to
predict future behavior or maintenance
needs, or potential impending danger, or
something like that-understanding that
behavior, and understanding those patterns
is critical. It could save the industry
a lot of downtime, " he said.
As GE Digital Vice President of Data
and Analytics Matt Schnugg, told POWER,
" While there will be many edge cases
that can also accommodate AI and/
or ML, the next step function in value
that the practice will return will be in
taking the focus from the performance
of one asset to the performance of the
fleet, and eventually to the performance
of the entire electricity network-from
generation to eventual consumption by
the consumer. " AI's potential to boost
" predictive and prescriptive capabilities "
is especially crucial today " because the
transaction of that generation has become
significantly more complicated
with the ubiquity of distributed generation
in the form of solar or wind, " he said.
Echoing these points, ABB's Friehauf
added that AI could also take simulations
to the " next level, " for example, to help
utilities locate unstable areas of the grid
and increase field worker safety. " From
daily operations, grid safety, reliability,
www.powermag.com
2. Inspired by the human brain, deep neural
networks learn from a large amount of data,
using algorithms to perform a task repeatedly,
each time adjusting it to improve outcomes-
almost in the way a human learns from experience.
Source: Stock/Pixabay
and more, there is a tremendous opportunity
for AI, " he said. MHPS's Sanchez
also pointed to AI's potential " significant
role " in the future integration of power
sector businesses, by, for example, leveraging
low-carbon resources, improving
forecasting, and boosting autonomous
operation. These could yield " more evident,
accessible, and economically viable "
safety and environmental benefits,
he said. Finally, AI offers the value of innovation,
Sanchez noted, for example,
helping to boost simulator capability
" to enhance their capacity and ability to
eventually synchronize online. "
AI's vast potential has spurred notable
initiatives by a long list of digital
solution providers, academic and government-backed
researchers, and utilities.
As examples, GE has implemented
neural networks and edge controls in
its Advanced Combustion Control systems
to optimize fuel splits for gas and
aeroderivative turbines, an effort it says
has reduced regulated nitrogen oxide
and carbon monoxide emissions, and improved
overall quality of performance. At
a fleet level, it offers a " Production Planning
Advisor " to help traders and dispatch
planners understand a combined
cycle plant's day-ahead and real-time capacity,
which helps them better prepare
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POWER February 2020

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