Transcript Slide 1

Edison Mission Energy:
PI for the Next Generation
(of Generation)
OSI Regional User Conference
10/09/08
Jerry Weber
Manager of Operations Support
Midwest Generation - EME
© 2008 OSIsoft, Inc. | Company Confidential
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Our mix of Generation: Thermal
 8 coal-fired plants
• 6 in Illinois (MWGen)
• 1 in Pennsylvania
• 1 in West Virginia
 9 gas-fired plants in
California and
Washington
© 2008 OSIsoft, Inc. | Company Confidential
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About Edison Mission Group
 A major Independent Power Producer (IPP),
headquartered in Irvine, CA
 30 Power Plants, 10,634 megawatts
 Energy marketing and trading center in Boston,
MA
 Sister company to Southern California Edison
© 2008 OSIsoft, Inc. | Company Confidential
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Wind: our new generation of Generation!
 18 Wind farms in Iowa,
Minnesota, New Mexico,
Oklahoma, Pennsylvania, Texas
and Wyoming.
 Projects are pending in Illinois,
Maine, Maryland, Nebraska,
New York, Pennsylvania, Utah,
West Virginia, Wisconsin and
Wyoming.
 We are one of the fastest
growing developers of
renewable energy.
© 2008 OSIsoft, Inc. | Company Confidential
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PI in our Coal Fleet
 We have a long and successful history (since mid
1990s) of using PI in our Coal power stations
– PI is a vital operations tool
 PI is used to:
– Collect and archive data from our station control
systems
– Trending and troubleshooting
– Predictive maintenance
– Interface with other analysis tools
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PI is very good at…
 Collecting
 Archiving
 Displaying
Tactical
(OK, we knew that)

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
Trending
Correlating
Calculating
Make Decisions
Strategic
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Coal Plant Performance Monitoring
Revolves Around PI
 PI stores data from control system
 Performance tags sent to Pmax
 Pmax calculates
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–
–
–
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Heat Rate
Controllable losses
Boiler and turbine efficiency
Furnace cleanliness
Other performance parameters
 Writes data back to PI for trending and storage
 Data presented to operations using Process Book
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PI examples in our Coal Fleet:
Superheat and Reheat Furnaces
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PI examples in our Coal Fleet:
Steam Turbines
Turbines
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PI examples in our Coal Fleet:
Controllable Loss Summary
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PI examples in our Coal Fleet:
LP Feedwater Heaters
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PI examples in our Coal Fleet:
High Pressure Feedwater Heaters
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How do we apply this to Wind?
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Wind and Coal Generation Differences
Wind
 Many generation units per site,
1-2.5 MW each
Coal
 1-3 generation units per site,
150-850 MW each
 All generation units are the
same at each site
 Generation units are often of a
unique design
 Generation units are relatively
simple in design
 Generation units have multiple
complex systems
 Data sources are few and fairly
uniform
 Data sources are many and
diverse
 Major equipment is 200-300 feet
in the air!
 Major equipment generally in a
building
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PI and Wind are a good fit
 Quick results: For same manufacturer, “Do one,
you’ve done them all”, all turbines have the same
tag list
 Need for monitoring: Wind sites are remote,
sometimes un-staffed
 Many needs for information: technical,
operational, financial
 Optimization opportunities: use PI information
strategically
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PI and Wind: our decision and strategy
1. Embed PI in EVERY Wind project
2. Became an Enterprise customer
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Challenge #1:
Establish a PI infrastructure design
Master PI Server
Irvine
Wind Site C
Wind Site A
PI Node
PI Node
Wind Site B
PI Node
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Challenge #2:
The PI – Turbine Tag Marriage
The challenge:
 We use Wind turbines from multiple manufacturers
 Each manufacturer/turbine model has a a unique tag list
and nomenclature
 Tags must be used to provide common performance
monitoring and measurement across multiple turbine types
The solution:
 A common strategy for tag collection and naming – tag
aliasing
 PI Module Database
 PI ACE
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Turning data into measurement and management
 Calculate various operating parameters
– Number of turbines running
– Number of turbines in various outage states
– Power curve
 The generation industry has standard calculations to measure performance,
including:
– Operating hours, outage hours, low and high wind hours, etc.
– Equivalent Availability Factor (EAF)
– Net Capacity Factor (NCF)
– Net Output Factor (NOF)
– Etc
 The PI Advanced Computing Engine (PI ACE) combined with the PI Module
Database (PI MDB) are being used to develop these and other performance
and measurement calculations for our Wind sites.
 Project is currently on-going with Global Automation Partners (GAP)
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Future opportunities:
Using pattern recognition
Turbine A
Generation
Wind Speed
Temperature
Turbine B
Generation
Wind Speed
Temperature
Turbine C
Generation
Wind Speed
Temperature
© 2008 OSIsoft, Inc. | Company Confidential
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Pattern Recognition
 Coal and wind units data correlates well and is fairly
consistent
 What if we could automatically determine when a
parameter was abnormal before we experienced an alarm
from the control system?
– Time to analyze the cause (instrument or process)
– Time to schedule maintenance
– Reduce unplanned events
 Software available to analyze a large PI data sampling to
determine patterns and make predictions
 Write predicted values to Pi for storage
 Automated reporting when parameters are out of limits
 Project underway with Scientech - PDP Pattern
recognition Software for both Wind and Coal Units
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