Download - GE gas turbine control
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Industrial Gas TurbinePerformance Improvements Through
Advanced Controls & Modeling
Tim HealyApril, 2009
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The Difference Between
Often Rests Heavily on The Control System
and Success,
Failure,
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3
Increasing Generation Diversity Requires
Increasing Flexibility From All Sectors
Cleaner CoalGasNuclear
HydroSolarBiomass Wind
Renewables
Nuclear
Therm
al
There Exists Significant
Opportunity To Improve
Performance & Emissions
In The Thermal Sector
Through Advanced Control
& Modeling
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4
Thermal Sector Remains A Very Large Part of
The Generation Portfolio
Projected World Electricity Generation by Fuel
0
5
10
15
20
25
30
35
2005 2010 2015 2020 2025 2030
Coal
Natural Gas
Liquids
Renewables
Nuclear
Source: History: Energy Information Administration (EIA), International Energy Annual 2005 (June-October 2007), Projections: EIA World Energy Projections Plus (2008)
Trillion Kilowatt-Hours
-
A Dramatically Revised Outlook for 09
3.2%
1.7% 1.6% 1.7%
6.4%
4.7%
8.7%8.2%
-0.5%
-2.5%-1.5%
-2.3%
2.2%
1.4%
6.5%
5.1%
World
USA Eurozone Japan
Russia MiddleEast
China India
2009 economic outlook
Source: Global Insight Outlook, April vs. December 23, 2008
World real GDP growth slowed from about 4% in 2006 and
2007 to 2.4% in 2008, expecting -0.5% in 2009
Last years outlook (April 2008)
Current outlook (January 2009)
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Outline
Industrial Gas Turbines Short-Course
Legacy Control Algorithms
Model-Based Control for Fuel Flexibility
The Road Ahead
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A Sense of Power
GE Evolution Locomotive ~5000 SHP
GE-90 Aircraft GT Engine~50,000 SHP
GE 9H Industrial GT Engine~500,000 SHP (combined-cycle)
Ford Shelby GT500~500 SHP
x 10
x 10 x 10
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T
S
Heat Source
COMPRESSION
GT
BRAYTON GAS CYCLE
TEMPERATURE
ENTROPY
COMBUSTION
1
STACK
2
3
4
Air
Comp
Comb
TurbGen
Fuel
1
2
3
4
Stack
Gas Turbine Plant - Simple Cycle
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9
Integrated Combined Cycle
1
3
4
T
S
Heat Source
Heat Sink
COMPRESSION
GT
BRAYTON GAS CYCLE
TEMPERATURE
ENTROPY
COMBUSTION
2HRSG
RANKINE STEAM CYCLESTACK
CONDENSER
ST
EXHAUST
GAS 9
105, 6
78
Gas Turbine & Steam Turbine - Combined Cycle
Pump
ST Gen
56
78
9
10
Cond
Air
Comp
Comb
GTGen
Fuel
12 3 4
HRSG
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10
Industrial Gas Turbine Overview
Compressor
Combustor
Turbine
Shaft
InletFlow
FuelFlow Exhaust
Flow
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CanAnnular Combustion Systems
Chamber Arrangement on Gas Turbine
Cross-Section Through One Chamber
Multiple Fuel Nozzles
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12
Industrial Gas Turbine Operability(Also Known as Control Requirements)
Compressor
Surge
Compressor
Aero-
Mechanics
Exhaust
Frame
Durability
Hot Gas
Path
Durability
Fuel
System
Operability
Combustion
Dynamics Combustor Lean Blow-
Out (LBO)
Combustor
Emissions
(NOx, CO, UHC)
Combustor
Flashback
(Flameholding)
Auto-
Ignition
Power Output
Optimal Efficiency
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Outline
Industrial Gas Turbines Short-Course
Legacy Control Algorithms
Model-Based Control for Fuel Flexibility
The Road Ahead
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Typical Industrial Gas Turbine Sensor/Effector Suite
CompressorInlet Guide Vanes (IGV)
Fuel Splits
Total Fuel Flow (Wf)
Inlet Pressure Drop
Compressor Discharge Temperature
Inlet Humidity
Inlet Temperature
Exhaust Pressure Drop
Ambient Temperature
Ambient Pressure
Exhaust Temperature
Actuator stroke feedback and some fuel system pressures not shown
Fuel Temperature
Compressor Discharge Pressure
Generator Losses
Generator Power
Inlet Bleed Heat (IBH)
Sensors
Effectors
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Sensor-Based Control Approach
Entropy
Temperature
P1=P4
Isentropic
Compression
Isentropic
Expansion
Constan
t Pressur
e
Heat Ad
dition
1
2
3
4
P2=P3
Maximum Cycle Temperature
Comp
Comb
Turb
1 2 3 4
Ideal Brayton Cycle
)T(T
)T(T)T(T
AddedHeat
OutputWork
23
1243Cycle
==
1
4
3
4
3
1
1
2
1
2
T
T
P
P
T
T
P
P
==
=
)1()1(
4
3
T
T1
=
Cycle
Higher T3 = Higher Cycle
T'
T turbine
=
( )
=
1
43
4
3
11-1
TT
P
Pturbine
Turbine Efficiency
T3 = f ( T4 , t , PRt )
T3 = f ( T4 , PRc )for assumed t and PRc ~= PRt
Problem:Desire To Control T3, But T3 is Not Measured
Solution:Correlate T3 to a Measured Variable
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Indirect (Schedule-Based) Boundary Control
Pre-Programmed Control Schedules
Field-Tuned For Performance & OperabilityT4_max
MIN
IMU
M
T4_req
T4
P+I+
-
Wf / IGV
X ~ Tx
Split
s
X
Fuel Splits
T4
PRc
PRc
Characteristics Simple
(Easily Understood and Verified)
Approximate Boundary Protection (Accommodates Worst-Case Condition)
Poor Accommodation Of Ambient/Fuel Variation (Impact to Emissions, Combustion Dynamics, LBO Margin)
No Explicit Accommodation Of Machine Deterioration (New & Clean / Mean Machine Assumption)
Coupled Effectors Prohibit Optimization (Part-Load Exhaust Temperature & Fuel Splits)
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Outline
Industrial Gas Turbines Short-Course
Legacy Control Algorithms
Model-Based Control for Fuel Flexibility
The Road Ahead
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Gas Fuel Composition Variation
Composition Variation Will Increase As More LNG Is Injected Into
Pipelines
Algeria
Malaysia
Nigeria
Norway
Oman
Qatar
Trinidad
USA
Abu
Dhabi
1300
1350
1400
1450
1500
US (Typical) Abu Dhabi Algeria Malaysia Nigeria Norway Oman Qatar Trinidad
Geographic Origin
Wo
bb
e In
de
x
Algeria
Malaysia
Nigeria
Norway
Oman
Qatar
Trinidad
Abu
Dhabi
USA
80
82
84
86
88
90
92
94
96
98
US (Typical) Abu Dhabi Algeria Malaysia Nigeria Norway Oman Qatar Trinidad
Geographic Origin
Meth
an
e
Co
nte
nt
[%]
Algeria
Malaysia
NigeriaNorway
Oman
Qatar
Trinidad
Abu
Dhabi
USA
0
2
4
6
8
10
12
14
US (Typical) Abu Dhabi Algeria Malays ia Nigeria Norway Oman Qatar Trinidad
Geographic Origin
Eth
an
e
Co
nte
nt
[%]
Algeria
Malaysia
Nigeria
Norway
Oman
Qatar
Trinidad
Abu
Dhabi
USA
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
US (Typical) Abu Dhabi Algeria Malays ia Nigeria Norway Oman Qatar Trinidad
Geographic Origin
Pro
pa
ne
Co
nte
nt
[%]
gS
HHVWI =
TS
LHV
g
MWI
=
LHVHHV ,
gS
T
Wobbe Index
Modified Wobbe Index
Fuel Higher/Lower Heating Value [BTU/Scf]
Fuel Specific Gravity
Fuel Temperature [R]
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What Is At Risk?
Gas turbine operability concerns due to composition variation:
Auto-Ignition Flashback
Emissions (NOx, CO) Combustion Dynamics Blow-out
Tuning is required to protect against fuel composition variation
Addressed by gas fuel specification
Addressed today by manual tuning
(given expected variation,
not an issue for most pre-
mixed combustion systems)
(given expected variation,
potentially a very serious issue)
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Gas Fuel Composition Rate-of-Change
Significant & rapid shifts in Null-Point
are possible
Rate and frequency of pipeline composition changes will increase
An automatic tuning process is required to support continuous & reliable operation
LNG
LNG
LNG
NG
NG
NG NP
NP
NP
Fictitious region / pipeline
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Legacy Solution Closed-Loop MWI With Fuel Temperature
Characteristics Costly
(Dual Gas Chromatographs)
Low-Bandwidth(GCs & Fuel Heat Exchangers)
Limited Authority(Performance Heater Capability)
Sub-Optimal Efficiency(Any Off-Nominal Fuel Temperature)
Performance Heater
IP Feedwater ControlDual GCs
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Direct (Model-Based) Boundary Control
IGV
Fuel Splits
+_
+_
+_
+_
+_
+_
+_
+_
+_
Loop Selection
Loop Selection
Loop Selection
Wf
ARES - ParameterEstimation
Engine Model
( )
16.0
27025.1
3
*394.6
95.3
3
*
*
T
SH
eP
eW
Physics-Based Boundary Models
Qe
eNOxNOx
ref
ref
SHSH
TflTfl
refO
**
*)(5.9
)*(006.
%15@ 2
=
Lim
it S
ch
ed
ulin
g
Virtual Sensors
Characteristics Robust / Flexible / Expandable
(Additional Boundaries / Loops)
Direct Boundary Protection(Physical Space of Boundary)
Good Accommodation Of Ambient / Fuel Variation(Manages Emissions, Combustion Dynamics, LBO Margin)
Accommodation Of Machine Deterioration(Adaptive Model Ensures Accurate Virtual Sensors)
Implicitly De-Coupled Effectors(Automatic Performance Optimization)
(SISO vs. MIMO: Industrial GT System Coupling & Time Scale Does Not Demand MIMO Control, Yet)
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Adaptive Real-time Engine Simulation (ARES)
ARES - ParameterEstimation
Engine Model
On-Line Partial Derivative Calculation
QaPaP T +=
RJPJs T +=
1= sJPK T
PJKPP =
(Covariance of
Prediction Error)
(Covariance of
Residual)
(Gain Matrix)
(Covariance of
Prediction Error)
On-Line Filter Gain Calculation
u
prtx
Ja,
+
_
+
+
u
exty
x
Z-1
MeasuredInputs
State Estimate
MeasuredOutputs
y
y
prty
Model Non-Linear Component-Level Cycle Model
Optimized for Real-Time Operation
Filter Extended Kalman Filter Formulation On-Line Jacobian & KF Gain Calculation
Re-configurable for Fault Accommodation Avoids Parallel Linear Model Process
Z-1P
K
ARES - Parameter
Estimation
Engine Model
Partial
Deriv.
Calc.
yx ,EstimatedOutputs
Extended Outputs
RQ,
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Model-Based Control Adapts Well To Environmental / Fuel Variation
+_
Lo
op
-In
-Co
ntr
ol
Str
uc
ture
ARES - ParameterEstimation
Engine Model
Virtual Sensors
Sensors
Effectors
+_
Physics-Based
Boundary Models
Lim
it S
ch
ed
ulin
g
max
min Control
CDM
NOx @15%O2 = f ( Tflame, Humidity,
Fuel_Fraction )
NOx
(target)
NOx
eNOxx eNOx
Fuel_Fraction
e1
e2
Tflame, Tfire,
W2, etc.
+_
+_
+_
+_
+_+_
+_
+_
Environment
GT
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Integrating Models, Sensors, & Algorithms
Fuel Temp.
Load Runback
( No Performance Impact )
( Small Performance Impact )
( Performance Impact )
Design Center
WobbeExpected LNG Range
Fuel Fraction
Closed-Loop Control
Boundary Sensor
X
X Boundary Model
RX+_
Adaptive-Model Approach
Performance optimization through hierarchical application of effectors
5
6
7
8
9
10
11
12
13
14
15
5 6 7 8 9 10 11 12 13 14 15
Measured NOx [ppm@15%O2]
Pre
dic
ted
NO
x [
pp
m@
15%
O2]
Site A
Site B
Site C
Site D
Site E (10% C2)
0.5
1.0
1.5
Measured Dynamics [psi]
Predicted Dynamics [psi]
0.5 1.0 1.5
Physics-Based Boundary Models
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Model-Based Control Performance
-6%
-4%
-2%
0%
2%
4%
6%
-20 0 20 40 60 80 100
Time [sec]
Wo
bb
e I
nd
ex
(W
I)
Ch
an
ge
[%
]
0
20
40
60
80
100
120
-20 0 20 40 60 80 100
Time [sec]
Co
mb
usti
on
Dyn
am
ics
Am
pli
tud
e [
% O
f T
arg
et]
Frequency 1
Frequency 2
6
7
8
9
10
-20 0 20 40 60 80 100
Time [sec]
NO
x [
pp
m@
15%
O2]
80
90
100
110
120
Gas T
urb
ine O
utp
ut
[%]
NOx
Load
Closed-loop simulation of model-based control algorithm (7FA+e DLN2.6, base-load, ISO Day)
~10% WI change imposed over ~30 seconds (rate >18%/minute)
OpFlex Wide Wobbe algorithm maintains emissions & dynamics levels using fuel distribution only
0
20
40
60
80
100
120
0 200 400 600
Time [sec]
Co
mb
usti
on
Dyn
am
ics
Am
plitu
de [
% O
f T
arg
et]
Frequency 1
Frequency 2
40
42
44
46
48
50
52
54
56
0 200 400 600
Time [sec]
MW
I R
each
ing
Co
mb
usto
r
0
50
100
150
200
250
300
350
400
Fu
el T
em
pera
ture
[d
eg
F]
MWI
Fuel Temp.
6
7
8
9
10
0 200 400 600
Time [sec]
NO
x [
pp
m @
15
%O
2]
7FA+e DLN2.6 gas turbine operating in combined-cycle at base-load
~260F fuel temperature excursion imposed (~20% MWI) over five minutes (max capability of fuel heat exchanger)
OpFlex Wide Wobbe system maintains emissions & dynamics levels using fuel distribution only
Field Test Closed-Loop Simulation
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Assessment
The Model-Based Control system provides many advantages over competing technologies with similar objectives:
Cost No additional auxiliary equipment required beyond control system sensor redundancy. No gas analyzer required
Operability Negligible change in output or efficiency as a result of changing fuel properties
Lower combustion dynamics across the operational envelope Improved output & efficiency at off-design conditionsReliability
Increased system availability due to sensor fault detection and accommodation
Emissions Tighter NOx control over a wider operational envelope
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Fuel Flexibility
Integrated Gasification / Combined-Cycle
Plant-Level Optimization
Grid-Code Compliance
Health Management
The Road Ahead
Advanced Controls & Modeling Will Play A Greater Role In Thermal Sector Technology / Solutions
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Fuel flex expanding the envelope
NG LNG wide wobbe High BTU hydrogen/EOR Low BTU Steel BFG/COG
Light crude Heavy crude vanadium & sulfur
Gas fuels Liquid fuels
Pet coke refining Coal syngas IGCC/SNG Biofuels ethanol
Synthetic fuels
Power producers seeking fuel diversification & flexibility
Increasing fuel prices & volatility driving substitution
Cleaner & more flexible technology lower emissions, increased turndown,
multi-fuel, durability
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30
Integrated Gasification Combined-Cycle
Sulfur
SulfurRemoval
Syngas
Electricity /Steam
Combined CyclePower Block
Gasifier
Solid feed Slag
Gas/Liquid feed - Ash
Pump
ST Gen
Cond
Air
Comp
Comb
GTGen
Syngas
HRSG
Cooling
Clean-
UpGasifier
Feed Prep.
Fuel + H2O
O2
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Plant-Level Optimization
GT loadreference
Final CC load
Control System
State estimation
Optimize GT loadingover Time Horizon
GT, HRSG, ST modelsHP & IP rotor stresses
MeasurementsSteam & metal Temperatures, Steam Pressures
MPC Controller
Measurements
HP & IP maximum rotor stresses
Physics-based models to predict stresses
real-time optimization to choose best loading profile
Handles multiple ST Stress constraints simultaneously
Handles multiple control actions simultaneously
Accommodates any initial thermal state of the plant
Model Predictive Controls for
Combined-Cycle Plant Start-Up Optimized Load Profile
Stress
constraints
Time
Time
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Back-Up
-
2007
-
2030
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Combustion Operability
NO
x NOx
Guarantee
Window
CO
CO
Guarantee
Fuel-Air Ratio
Dyn
am
ics Hot Tone
Dynamics
Limit
NOx
CO
Dynamics
OperabilityOperability
WindowWindowLean
Blow Out
Window
Window
Tfi
re (
Po
wer)
Lean Blow Out
Window
Cold ToneDynamics
Fuel-Air RatioFuel-Air Ratio
Fuel-Air Ratio