Data-Driven Hierarchical Neural Network Modeling for High ...€¦ · Thermal Power Plant...
Transcript of Data-Driven Hierarchical Neural Network Modeling for High ...€¦ · Thermal Power Plant...
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Data-Driven Hierarchical Neural Network Modeling for High-Pressure Feedwater Heater Group
Authors: Jiao Yin, Mingshan You, Jinli Cao, Hua Wang,
MingJian Tang and Yong-Feng Ge
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4 Feb 2020 2
Contents
Introduction
Industrial Background
Data-Driven Hierarchical Neural Network Modeling
Experiments and Results
Conclusions
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Introduction
Data-driven machine learning applications Image identification
Speech recognition
Natural Language Understanding
…
Machine learning in thermal power industry High-pressure feedwater heater group modeling
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Introduction
High-Pressure Feedwater Heater Group (HPFHG)[2]
Consists of three high-pressure feed-water heaters(HPFHs)
o Cascade structure
HPFHG Modeling requirements
o Modeling the heater group as a whole √
o Modeling each single heater at the same time √
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Introduction
HPFHG Modeling Techniques
Physical modeling techniques
o Based on the first law of heat transfer, the second law of heat transfer, the law of
conservation of mass and Newtonian cooling equation
Flaws:
o Some coefficients are dynamically changing [12,9].
o Some coefficients have no sensor to measure [1].
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Introduction
HPFHG Modeling Techniques
Data driven methods
o Traditional ‘black box’ artificial neural network (ANN) model
Flaws:
o Modeling the heater group as a whole √
o Modeling each single heater at the same time ×
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Introduction
Our method
Data-Driven Hierarchical Neural Network Modeling Approach
o Inspired by the physical cascade structure of the heater group
o Modeling the heater group as a whole √
o Modeling each single heater at the same time √
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Contents
Introduction
Industrial Background
Data-Driven Hierarchical Neural Network Modeling
Experiments and Results
Conclusions
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Industrial Background Thermal Power Plant Regenerative System
Improve thermal efficiency
Save fuel
Reduce pollutionBoiler Turbine
Turbo Generator
Steam Condenser
Condensate Pump
LPFHGHPFHG
Deaerator
Feedwater Pump
#1 #2 #3
steam
rege
nera
tive
extra
ctio
n ste
am
feed
wat
er
condensated water
discharging steam
rege
nera
tive
extra
ctio
n ste
am
feed
wat
er
G
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Industrial Background
HPFHG modelling objective Find out the relationship between the feedwater outlet temperature and other
variables
HPFHG modelling significance: [4,5,7] Find out the best working condition
Fault detection
Improve efficiency
Reduce emissions
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Industrial Background Variables for a single High-Pressure Feedwater Heater Modeling
Relative
Available
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Industrial Background High-Pressure Feedwater Heater Group
#3 #32 2,w wt P
#3 #3,h st P
#3 #31 1, ,w w wt P G#2 #2
2 2,w wt P
#2 #2,h st P
#2st
#2L
#2odt
#2 #21 1,w wt P
#1 #1,h st P#1L
#1odt
#1 #11 1,w wt P#1 #1
2 2,w wt P#3L
#3odt#3
st
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Industrial Background High-Pressure Feedwater Heater Group
#3 #32 2,w wt P
#3 #3,h st P
#3 #31 1, ,w w wt P G#2 #2
2 2,w wt P
#2 #2,h st P
#2st
#2L
#2odt
#2 #21 1,w wt P
#1 #1,h st P#1L
#1odt
#1 #11 1,w wt P#1 #1
2 2,w wt P#3L
#3odt#3
st
A Shared Variable
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Industrial Background
#3 #32 2,w wt P
#3 #3,h st P
#3 #31 1, ,w w wt P G#2 #2
2 2,w wt P
#2 #2,h st P
#2st
#2L
#2odt
#2 #21 1,w wt P
#1 #1,h st P#1L
#1odt
#1 #11 1,w wt P#1 #1
2 2,w wt P#3L
#3odt#3
st
High-Pressure Feedwater Heater Group
Cascade variable pairs
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Industrial Background
#3 #32 2,w wt P
#3 #3,h st P
#3 #31 1, ,w w wt P G#2 #2
2 2,w wt P
#2 #2,h st P
#2st
#2L
#2odt
#2 #21 1,w wt P
#1 #1,h st P#1L
#1odt
#1 #11 1,w wt P#1 #1
2 2,w wt P#3L
#3odt#3
st
High-Pressure Feedwater Heater Group
Cascade variable pairs
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Industrial Background Variables for Single Heater #3 / #2 / #1 Modeling
#3 #32 2,w wt P
#3 #3,h st P
#3 #31 1, ,w w wt P G#2 #2
2 2,w wt P
#2 #2,h st P
#2st
#2L
#2odt
#2 #21 1,w wt P
#1 #1,h st P#1L
#1odt
#1 #11 1,w wt P#1 #1
2 2,w wt P#3L
#3odt#3
st
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Industrial Background Variables for HPFHG Modeling
#3 #32 2,w wt P
#3 #3,h st P
#3 #31 1, ,w w wt P G#2 #2
2 2,w wt P
#2 #2,h st P
#2st
#2L
#2odt
#2 #21 1,w wt P
#1 #1,h st P#1L
#1odt
#1 #11 1,w wt P#1 #1
2 2,w wt P#3L
#3odt#3
st
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Contents
Introduction
Industrial Background
Data-Driven Hierarchical Neural Network Modeling
Experiments and Results
Conclusions
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Data-Driven Hierarchical Neural Network Modeling
Architecture
Consists of 3 subnets
o net #3 HPFH #3
o net #2 HPFH #2
o net #1 HPFH #1
shared input
hidden layer of #3
output of #3
hidden layer of #2
other inputs of #1
output of #2
hidden layer of #1
output of #1
other inputs of #2
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Data-Driven Hierarchical Neural Network Modeling
Loss function: multi-task learning
Jointly training net #3, #2 and #1o Modeling the heater group as a whole √
o Modeling each single heater at the same time √
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Contents
Introduction
Industrial Background
Data-Driven Hierarchical Neural Network Modeling
Experiments and Results
Conclusions
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Experiments and Results
Experimental Data collected from a thermal power unit whose capacity is 1000MW
collected over a month without interruption
sampling interval is 5 minutes
m=10081
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Experiments and Results
Performance Evaluation Criteria
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Experiments and Results
Experimental Setting
1
1
Overfitting strategy: early stop
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Experiments and Results
Contrast experiment
A “black box” ANN model with three hidden layers
1x
2x
nx
( )h xΘ
( 2)(2)n
a
(3)1a
(3)2a
(3)(3)n
a
(2)1a
(2)2a
(4)1a
(4)2a
( 4)(4)n
a
(5)1a
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4 Feb 2020 26The proposed method The ‘black box’ ANN
HPFH #3
HPFH #2
HPFH #1
HPFH G
HPFH #3
HPFH #2
HPFH #1
HPFH G
Outlet temperature Percentage Error Outlet temperature Percentage Error
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Experiments and Results results comparison
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Contents
Introduction
Industrial Background
Data-Driven Hierarchical Neural Network Modeling
Experiments and Results
Conclusions
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4 Feb 2020 29
Conclusions
Contributions Defined an industrial application problem
Provided a data-driven hierarchical neural network modeling approach to model HPFHG and each single HPFH at the same time
The proposed model can be used to find out the best operating condition, detect system faults, save fuel and reduce pollution.
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4 Feb 2020 30