POWER September 2015 - 60

OPERATIONS & MAINTENANCE
Optimize Power Plant Operations
with Industrial Data Management
and Predictive Analytics
Power companies that leverage existing data generation and collection tools
for input into predictive analytics software are achieving early warning
notification of potential equipment problems days, weeks, or months before
failure.
Justin Thomas
I
n today's digital world, an optimally run
power plant relies on valuable and accurate
data in order to ensure assets are running
reliably and with minimal downtime.
Streaming in from control systems, SCADA
systems, distribution management systems,
energy management systems, equipment
sensors, plant historians, and many other
sources, this data must be organized and
available to many resources if optimization
is to be effective.
When managing a large fleet of assets
across numerous sites, making sense of the
streams of operational data coming in from
multiple systems and from remote sources
can become an impossible task. Most utilities
today are already using real-time historians to
consolidate disparate data sources for a more
comprehensive view of plant performance.
Building on that data management foundation,
predictive asset analytics solutions can
leverage data from the historian to further
improve operations by providing early warning
detection of equipment issues before they
lead to failure.
To achieve the next level of operational efficiency
and make the most of the increasing
amount of information available, generating
companies are investing in predictive maintenance
programs, including the implementation
of predictive asset analytics software. Predictive
analytics software helps organizations to
gain the greatest return on every single asset
and, in turn, improve overall plant operations.
Data Management Foundation
The first step in implementing a predictive
maintenance program is having the data
management foundation in place. At most
utilities, relevant asset information is typically
stored within a historian. The historian
captures and archives continuously streaming
real-time data from any type of asset or
system, regardless of manufacturer. This is
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POWER | September 2015
beneficial when maximizing the usefulness
of time-series data that is often most relevant
in real time. A single data management system
ensures that all necessary information
can be made available and compared with
other data streams quickly and accurately.
This increases situational awareness and
builds the foundation for holistic visibility
of the entire enterprise.
Predictive Asset Analytics
After ensuring that a solid data management
infrastructure is in place, companies can
implement predictive analytics software that
uses continuously streaming time-series data
for real-time insights. Predictive asset analytics
software helps organizations improve
operational performance by using predictive
algorithms to calculate and predict normal
asset behavior. For example, Schneider Electric's
Avantis PRiSM software is based on
advanced algorithms that use advanced pattern
recognition and machine learning technology,
which has been shown to provide
weeks to months of early warning notification
of deviations in operational performance
in power plants (Figure 1).
The software uses empirical data to learn
normal performance modes or patterns for the
equipment that is being modeled. That empirical
data is captured from the data historian
and should not require additional investments
in specialized sensors. Although equipment
modeling can be a complicated task, some solutions
are designed with tools and templates
that simplify and streamline the process. This
allows models to be created in a matter of minutes,
rather than days or weeks.
When implementing a predictive asset
analytics solution, the organization identifies
1. Early warning enables timely action. This illustration shows how Avantis PRiSM
identifies anomalies and provides notification of abnormal conditions before operational alarms.
Early warning notification leads to improved maintenance planning and more efficient prioritization
of maintenance activities. Courtesy: Schneider Electric

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POWER September 2015

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