POWER June 2011 - 26

INFORMATION TECHNOLOGY
very high reliability. However, simply relying
only on fully mature technology, as is
our habit, may result in a significant lost opportunity
to modernize the power generation
infrastructure. Technology advancements
are already under way with many smart grid
projects that promise to improve the process
and cost of delivering electricity. Similarly,
new and emerging technologies that leverage
IT should be considered when planning for
future power plant controls.
As a major international engineering,
procurement, construction and commissioning
firm and technology integrator for
gas-fueled, solid-fueled, and nuclear power
facilities, Fluor Corp. has had the unique
opportunity to work with just about all major
suppliers for power plant distributed
control systems (DCSs), programmable
logic controllers, instrumentation/automation,
and network components communicating
in various protocols over a range of
different control system architectures. Two
previous articles ( " Digital Networks Prove
Reliable, Reduce Costs, " July 2009 and " TS
Power Plant, Eureka County, Nevada, " October
2008, available in POWER's archives
at www.powermag.com) reviewed what are
generally considered to be state-of-the-art
digital control power system designs.
Artificial Intelligence Primer
In very simplistic terms, AI enables computers
to think like humans to solve problems.
When applied to power facilities, AI,
perhaps more correctly called intelligent
control, collects data from the plant and,
when combining that data with stored data
from library resources, is able to " learn "
the appropriate response based on a predetermined
desired state or some external request
to achieve another state of operation.
Many researchers consider systems that
learn from their actions and make good
decisions with incomplete information as
having a form of artificial intelligence.
Neural networks take inputs from the environment
and process them to determine
patterns and relationships and then point to
a directed output (Figure 2). In human intelligence,
neurons in our brains learn how to
respond to daily stimuli. Neural networks allow
computers to artificially mimic this same
process recognition and classification of
data. The AI neurons are connected by varying
strengths (as represented by various line
weights in the figure).
Although the diagram is simplistically
shown in two dimensions, there are actually
multiple layers-in space and time-
for the connections. Depending on the
complexity of the stimuli, there could be
a few connections or an extensive network
of connections. Processing numerous connections
in parallel and keeping track
of the results allows the neural network
to " learn " from experience and store or
memorize the end result. The network can
subsequently retrieve millions of prior experiences
quickly to make value judgments
in similar circumstances in the future.
Classical control systems have been processing
data and determining outputs for
quite some time. Conventional control practices
work best when the systems are not
overly complex. These control systems rely
on alarms to ensure operator notification if
the process is approaching or achieves an
" out-of-control " or abnormal state.
The more complex the application and
2. Capturing experience. Neural networks artificially mimic the human brain in process
recognition and classification of data. The complexity of the stimuli (input) determines the number
and strength of the pathways (illustrated by line weight) that determine the result (output).
Processing numerous connections in parallel and keeping track of the results allows the neural
network to learn from " experience. " Source: Fluor Corp.
Input
Output
the more diverse the conditions under
which it operates, the more complicated
the control system becomes. When the
process being controlled is very complex
and has to operate under a wide range of
conditions and disturbances, AI technology
enhances classical control theory techniques.
This type of control, based on AI
layering, is referred to as intelligent control.
Incorporating AI into control systems
allows these systems to be more flexible,
to adapt to various operating conditions
and disturbances, and to incorporate human
expertise and thinking into their decision-making
process.
AI terminology has been somewhat foreign
to the power industry. The lines between
classical control and AI are sometimes
blurred. Familiar fuzzy logic design is an
area where classical controls have incorporated
elements of AI.
Data Proliferation
At the core of intelligence is raw data. For
power generating facilities, we have seen an
exponential increase in available plant data
stored in plant databases and historians.
This growth in data has come from many
different sources.
The first source is the general growth
in plant automation as distributed control
systems replaced manual plant controls.
Another source of growth in data has been
from the advancement of predictive and
preventive maintenance in power stations.
As digital communications have spread
throughout the design architecture of new
facilities, more data is more easily accessible,
and this has led to significant increases
in available data sources or inputs. Finally,
in our current state of interactive human/
nonhuman controls, significant data is collected
through plant alarm systems designed
to assist operations when the plant is outside
of the normal bounds of control. Those
situations can present significant challenges
to operators if the alarms are not properly
managed through prioritization and filtering
of extraneous or duplicate alarms.
In addition to the growth in field data collected,
volumes of data from the many components
that make up today's modern power
plant are available as resource information.
This data sometimes includes initial checkout
and testing data, preventive maintenance
requirements, maintenance records, and
equipment operating curves.
As an example of how voluminous the
Input layer
26
Hidden layer
Output layer
www.powermag.com
data can be in state-of-the-art plant control
systems, Fluor recently completed a 2 x 800MW
supercritical coal facility that includes
more than 15,000 physical instrument tags.
This facility incorporates some of the most
POWER | June 2011

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POWER June 2011

Table of Contents for the Digital Edition of POWER June 2011