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INDUSTRY INFOSHEET:
Renewable Energy
Generate clean energy
more efficiently with
powerful data analytics
Design more robust
and better performing
equipment using
experimental design and
multivariate analysis >
Reduce costly
unscheduled maintenance
and machinery downtime
with powerful multivariate
prediction models >
Optimize equipment
and machinery use by
understanding process
and environmental
factors affecting them >
CAMO > Renewable Energy
Generate sustainable energy more
efficiently and cost effectively
Advanced data analysis tools can help renewable energy
companies operate more efficiently, from R&D - such as
developing more efficient solar panels and more robust wind
turbine blades - to solving common problems such as predicting
turbine breakdowns and optimizing the use of equipment
depending on local conditions and demand.
Multivariate data analysis is used extensively in industries such as
pharmaceuticals, chemicals, biotechnology, semi-conductors and
agriculture to understand large, complex data sets that traditional
statistics are inefficient or unable to handle. Now, these powerful but
relatively easy to use tools are being applied by the energy sector to
help improve their product and process performance.
CAMO Software’s solutions can be integrated with existing control
systems, accepting proprietary data formats from sensors, industrial
databases (ODBC and OSI PI) and process instruments, using protocols
such as Modbus, Profibus, TCP/IP and now OPC (DA, HDA and UA
capabilities). The graphical outputs make interpretation simple and can
be customized for all user levels, from expert to operator.
REAL BUSINESS BENEFITS
Our solutions can be used across the renewable energy generation value chain from product
development to process and equipment monitoring.
Product development
Process monitoring
Equipment monitoring
>
Use Design of Experiments and
multivariate data analysis to design more
efficient solar cells
>
Avoid production shutdowns with early
event detection using multivariate
predictive models
>
Understand the impact of
environmental conditions to increase
the lifetime of equipment
>
Develop more robust turbines or blades
using advanced materials
>
Implement preventative maintenance
programmes based on powerful
regression models
>
Get an overview if a process is in
control or requires intervention
What is Multivariate Data Analysis?
Multivariate data analysis is the investigation of many variables, simultaneously, in order to understand the
relationships that exist between them. While traditional (univariate) statistical approaches such as mean, median,
standard deviation etc serve their purposes for investigating and understanding simple systems, when the
relationships between variables are complex a single variable cannot adequately describe the system. Exploratory
data analysis (data mining), clustering, regression and predictive analysis are typical multivariate tools.
> Bring data to life > camo.com
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FREE E-BOOK
CAMO > Renewable Energy
case studies and
example applications
Case study: Wind turbine monitoring
A major European renewable energy firm operates wind farms
with several hundred turbines in each. In one particular farm
of 226 turbines, 28 had faults resulting in breakdowns. The
client wanted to be able to predict which turbines were likely to
malfunction in the future.
Each turbine had a number of sensors mounted on them so they
could measure vibrations, temperature, pressure, load etc. In
addition, each turbine had associated historical data including the
number of hours of operation, total amount of energy produced
and the location.
Case study: Product development for wind turbine blades
A global wind turbine manufacturing client used multivariate data
analysis and designed experiments (DoE) to develop turbine blades
constructed from advanced materials. Using DoE they were able to
determine the optimal design parameters to produce more reliable,
durable blades while analyzing quality parameters with multivariate
analysis.
To begin the project, all the data from the sensors over a certain
period of time was collected together with the historic data and
analyzed using multivariate methods, from which a multivariate
predictive model was developed.
Then, using statistical methods, we were able to define for the
client when production was ‘normal’ i.e. within the sweet spot of
optimal operating parameters.
Next, new observations (sensor data) could be projected onto the
multivariate model developed earlier to identify deviation from
normal situations. In situations where data did go outside normal
parameters, operators were able to see which variable (e.g. load)
had changed. The resulting model was able to identify 100% of
the wind turbines with failures and this could be applied to realtime monitoring of turbines and even the entire wind farm.
Example application: Predictive maintenance in a
hydro-electric facility
A major European hydro-electric company had persistent problems
with a turbine breaking down resulting in long periods of downtime
and expensive maintenance. Multivariate analysis could be used
to analyze historic data to determine the conditions leading to
breakdown. After instrumenting the turbine, this model could be
refined with additional data points from sensor readings over time
to make a more accurate model. This enables systems to be put in
place to alert operators in real-time when a breakdown is imminent,
thereby allowing them to implement preventative maintenance
before failure occurred.
DON’T WASTE YOUR VALUABLE DATA
Most energy manufacturers collect an enormous amount of data from sensors, yet the majority do not
exploit its full potential due to the perceived difficulty and lack of statistical knowledge. However, today’s
data mining and analytical tools are much simpler to use and even more powerful, enabling industry
leaders to get valuable insights from their data which are driving significant business improvements.
> Bring data to life > camo.com
CAMO SOFTWARE
PRODUCTS & SERVICES
Get deeper insights from your data with our range of powerful, yet easy to use and affordable data
mining and predictive analysis solutions.
The Unscrambler® X
Unscrambler® X Process Pulse
Leading multivariate analysis software used by
thousands of data analysts around the world
every day. Includes powerful regression,
classification and exploratory data analysis tools.
TRIAL VERSION
READ MORE
Real-time process monitoring software that
lets you predict, identify and correct
deviations in a process before they become
problems. Affordable, easy to set up and use.
TRIAL VERSION
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Training
Our Partners
Consultancy and Data Analysis Services
Our experienced, professional trainers can help your
team use multivariate analysis to get more value from
your data. Classroom, online or tailored in-house training
courses from beginner to expert levels available.
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CONTACT US
CAMO Software works with a wide range of
instrument and system vendors. For more
information please contact your regional
CAMO Software office or visit
www.camo.com/partners
Do you have a lot of data and information but don’t
have resources in house or time to analyze it? Our
consultants offer world-leading data analysis skills
combined with hands-on industry expertise.
READ MORE
CONTACT US
Unscrambler® X Prediction Engine
& Classification Engine
Software integrated directly into analytical or
scientific instruments for real-time predictions and
classifications directly from the instruments using
multivariate models from The Unscrambler® X.
TRIAL VERSION
READ MORE
Find out more
For more information please contact your regional
CAMO office or email [email protected]
www.camo.com
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