12th heat rate performance improvement conference

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EPRI's Twelfth Heat Rate Improvement

Conference Proceedings

Proceeding

Januar y 15-1 9, 2001 • New Orleans, Louisiana • The Hotel Inter-Continental New Orleans


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EPRI Project ManagerJ. Stallings

EPRI 3412 Hillview Avenue, Palo Alto, California 94304 • PO Box 10412, Palo Alto, California 94303 • USA800.313.3774 • 650.855.2121 • [email protected] • www.epri.com

EPRI’s Twelfth Heat RateImprovement Conference

Proceedings

1001328

Proceedings, January 2001


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DISCLAIMER OF WARRANTIES AND LIMITATION OF LIABILITIES

THIS DOCUMENT WAS PREPARED BY THE ORGANIZATION(S) NAMED BELOW AS ANACCOUNT OF WORK SPONSORED OR COSPONSORED BY THE ELECTRIC POWER RESEARCHINSTITUTE, INC. (EPRI). NEITHER EPRI, ANY MEMBER OF EPRI, ANY COSPONSOR, THEORGANIZATION(S) BELOW, NOR ANY PERSON ACTING ON BEHALF OF ANY OF THEM:

(A) MAKES ANY WARRANTY OR REPRESENTATION WHATSOEVER, EXPRESS OR IMPLIED, (I)WITH RESPECT TO THE USE OF ANY INFORMATION, APPARATUS, METHOD, PROCESS, ORSIMILAR ITEM DISCLOSED IN THIS DOCUMENT, INCLUDING MERCHANTABILITY AND FITNESSFOR A PARTICULAR PURPOSE, OR (II) THAT SUCH USE DOES NOT INFRINGE ON ORINTERFERE WITH PRIVATELY OWNED RIGHTS, INCLUDING ANY PARTY'S INTELLECTUALPROPERTY, OR (III) THAT THIS DOCUMENT IS SUITABLE TO ANY PARTICULAR USER'SCIRCUMSTANCE; OR

(B) ASSUMES RESPONSIBILITY FOR ANY DAMAGES OR OTHER LIABILITY WHATSOEVER(INCLUDING ANY CONSEQUENTIAL DAMAGES, EVEN IF EPRI OR ANY EPRI REPRESENTATIVEHAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES) RESULTING FROM YOURSELECTION OR USE OF THIS DOCUMENT OR ANY INFORMATION, APPARATUS, METHOD,PROCESS, OR SIMILAR ITEM DISCLOSED IN THIS DOCUMENT.

ORGANIZATION(S) THAT PREPARED THIS DOCUMENT

Electric Power Research Institute

ORDERING INFORMATION

Requests for copies of this report should be directed to the EPRI Distribution Center, 207 CogginsDrive, P.O. Box 23205, Pleasant Hill, CA 94523, (800) 313-3774.

Electric Power Research Institute and EPRI are registered service marks of the Electric PowerResearch Institute, Inc. EPRI. ELECTRIFY THE WORLD is a service mark of the Electric PowerResearch Institute, Inc.

Copyright © 2001 Electric Power Research Institute, Inc. All rights reserved.


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CITATIONS

These proceedings were compiled by

EPRI3412 Hillview Avenue

Palo Alto, California 94303

This report describes research sponsored by EPRI.

The report is a corporate document that should be cited in the literature in the following manner:

EPRI's Twelfth Heat Rate Improvement Conference Proceedings, EPRI, Palo Alto, CA: 2001.

1001328.


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REPORT SUMMARY

The Twelfth Heat Rate Improvement Conference, sponsored by EPRI’s Heat Rate and CostOptimization Value Package, is the latest in a series of meetings designed to assist utilities in

addressing problems with power plant performance and in identifying cost-effective solutions for

achieving and maintaining heat rate improvement. The previous conference was held in

Baltimore in September 1998.

Background

Deregulation in the utility industry has forced power plants to lower their costs of generatingelectricity to become more competitive. Since the cost of fuel for coal-fired plants accounts for60-80% of the overall cost of electricity, improvements in heat rate are at the forefront of these

cost-cutting efforts. In the long run the lowest-cost generators will be the ones that dominate the

power industry.

ObjectiveTo summarize current efforts by EPRI and others to improve the heat rate of fossil-fired power

plants, including optimization, intelligent sootblowing, and heat rate performance and

monitoring.

ApproachEPRI’s Heat Rate and Cost Optimization Value Package hosted a conference January 30 to

February 1, 2001 in Dallas, Texas. The conference was divided into six technical sessions, withthree additional panels designed to investigate individual topics in more depth. Panel topics

were:

• How are utilities using heat rate information?

• Why test?

• What improvements in heat rate information are needed?

Results

• Areas addressed in the individual sessions include:

• The effectiveness and usefulness of On-Line Heat Rate Monitors

• The trend for Optimization software tools to use heat rate as an input into total plant costminimization efforts

• The potential for incorporating Intelligent Sootblowing applications into optimizationefforts


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• The possibilities for heat rate improvements from upgrades in Turbines and Auxiliaries

• The latest trends in Heat Rate Testing

• Actual Plant Experiences with heat rate improvement projects

EPRI PerspectiveThis conference was sponsored by EPRI’s Heat Rate and Cost Optimization Value Package. Assuch, the meeting reflects those topics considered most important by the members of the value

package in their continual efforts to improve heat rate and overall plant performance. The value

package is currently supporting demonstrations of on-line heat rate monitors, total plant costoptimization, intelligent sootblowing, and steam quality assessment. The proceedings from the

previous conference in 1998 were published as Proceedings: 1998 Heat Rate Improvement

Conference (TR-111047).

KeywordsHeat Rate

Boiler PerformanceFossil-Fired Power PlantsPower Plant Optimization

Sootblowing


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AGENDA

EPRI’s Twelfth Heat Rate Improvement Conference

January 30-February 1, 2001Hotel Inter-Continental Dallas

Dallas, TX

Final Agenda

Tuesday, January 30, 2001

7:00 a.m. Registration and Continental Breakfast

8:00 a.m. Welcome

Conference Chair: Jeff Stallings, EPRI

Utility Host: Ron Seidel, Senior Vice President, Fossil Generation, TXU Energy

8:10 a.m. Session 1: On-Line Heat Rate Monitors

Session Chairs: Tom Calle, TXU Energy and Charlie Rose, Consultant

8:10 a.m. Real-Time Performance Monitoring of Coal-Fired Units Duane Hill, Dairyland Power Cooperative

Sastry Munukutla, Tennessee Technological University

8:35 a.m. F-Factor Method for Heat Rate Measurement and its Characteristics

Nenad Sarunac, Lehigh UniversityCarlos E. Romero, Lehigh University

Edward K. Levy, Lehigh University

9:00 a.m. The Input/Loss Method

Fred D. Lang, Exergetic Systems, Inc.

9:25 a.m. Recent Experiences with Performance Monitoring at TVA using EPRI’s

Plant Monitoring Workstation Robert Inklebarger, TVA

Eric Sikes, TVA

Cyrus Taft, EPRI I&C Center


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9:50 a.m. Break

10:20 a.m. Modular Heat Rate Calculations for Power Plants using OPC

Harry R. Winn, Westinghouse Process Control, Inc.

10:45 a.m. Improving Results, Confidence and Decisions with Precise Data Validation Duane Hill, Dairyland Power CooperativeMarcus Caudill, Performance Consulting Services, Inc.

Ron Griebenow, Performance Consulting Services, Inc.

11:10 a.m. Panel: How are utilities using heat rate information?

Moderator: Mark Ness, Great River Energy

Panelists:Tom Calle, TXU Energy

Duane Hill, Dairyland Power Cooperative

Leeth DePriest, Southern Company Services

12:00 p.m. Lunch

1:00 p.m. Session 2: Optimization

Session Chairs: Darrell Howard, TVA and Stratos Tavoulareas, EnTEC

1:00 p.m. Impacts of Combustion Optimization on Power Plant Heat Rate Carlos E. Romero, Lehigh UniversityEdward K. Levy, Lehigh University

Nenad Sarunac, Lehigh University

1:25 p.m. Obtaining Improved Boiler Efficiency and NOx using Advanced EmpiricalOptimization and Individual Burner Instrumentation on a Boiler Operated

in Load-Following Mode E. P. Payson, Allegheny Energy Supply

Dave Earley, Air Monitor Corporation

Rich Brown, EPRI

Carlos Moreno, Ultramax Corporation

1:50 p.m. Application of GNOCIS™™™™ Neural Network Optimization Controller for

Boiler Efficiency Control Darrell A. Howard, TVA

Lonnie Coffey, EPRI I&C Center/TVA

2:15 p.m. Heat Rate Improvement at Dairyland’s Madgett Station using NeuSIGHT™™™™

Duane Hill, Dairyland Power Cooperative

Brad Radl, Pegasus Technologies

Glen Foster, Data Systems & Solutions

2:40 p.m. Break


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3:10 p.m. ProcessLink™™™™ at the Roanoke Valley Energy Facility Don Keisling, LG&E

Peter Spinney, NeuCo

3:35 p.m. Automatically Control NOx with Heat Rate Constraints, in a Coal-Fired

Power PlantKandi Forte, Reliant Energy

Tom Cowder, Reliant Energy

Russell F. Brown, Pavilion Technologies, Inc.

4:00 p.m. Unit Optimization at Hammond Unit 4

John Sorge, Southern Company Services

5:30 p.m. Reception and Exhibits

Wednesday, January 31, 2001

7:00 a.m. Continental Breakfast

8:00 a.m. Session 3: Intelligent Sootblowing Session Chairs: William Yee, Reliant Energy and Rabon Johnson, EPRI I&C

Center

8:00 a.m. Effects of Sootblowing in Coal-Fired Boilers on Unit Heat Rate and Nox Carlos E. Romero, Lehigh University

Nenad Sarunac, Lehigh University

Edward K. Levy, Lehigh Univerisity

8:25 a.m. Optimization of Boiler Sootblower Operation

Jeffery Williams, Westinghouse Process Control, Inc.

Xu Cheng, Westinghouse Process Control, Inc.

Bernie Begley, Southern California Edison, Inc.Alex Smith, Southern California Edison, Inc.

Dale Hopkins, Southern California Edison, Inc.

8:50 a.m. Intelligent Sootblowing Application Development Neel J. Parikh, Pegasus Technologies, Inc.

Brad J. Radl, Pegasus Technologies, Inc.

9:15 a.m. Intelligent Sootblowing – Boiler Cleaning Management System Randy Carter, Applied Synergistics

9:40 a.m. Break

10:10 a.m. Session 4: Turbines and Auxiliaries

Session Chairs: Jim Terrell, TVA and Tom McCloskey, EPRI


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10:10 a.m. In-Situ Feedwater Flow Measurement Duane Hill, Dairyland Power Cooperative

Izidro Diaz–Tous, Encor-America

10:35 a.m. In-Situ Enthalpy Measurements in Low Pressure Condensing Steam

Turbines Steve Hesler, EPRI

Tom McCloskey, EPRI

11:00 a.m. Steam Turbine Related Reseach at TVA Jim Terrell, TVA

11:25 a.m. On-Line Performance Monitoring and Condition Assessment of Steam

TurbinesRolf F. Orsegh, Impact Technologies LLC

Michael Roemer, Impact Technologies LLC

Ben Atkinson, Impact Technologies LLCBill McGinnis, Reliant Energy

Scott McQueen, Reliant Energy

11:50 a.m. Lunch

1:00 p.m. Session 5: Heat Rate Testing

Session Chair: Sam J. Korellis, Dynegy Midwest Generation

1:00 p.m. Performance Evaluation and TestingSam J. Korellis, Dynegy Midwest Generation

Philip Gerhart, University of Evansville

1:25 p.m. A Procedure for Analyzing Power Plant Measurement Variances Associated

with Thermal Performance Testing Fred D. Lang, Exergetic Systems, Inc.

1:50 p.m. Cycle Alignment Methods and Evaluation

Sam J. Korellis, Dynegy Midwest Generation

2:15 p.m. In Search of “Unaccounted for” BTUs via the Art of ASME PTC-6 Testing

Italo Liberatore, Constellation Power Source Generation

Allison Rossi, Constellation Power Source GenerationDonald Fyhr, Constellation Power Source Generation

2:40 p.m. Break


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3:10 p.m. Panel: Why test? Moderator: Sam Korellis – Dynegy Midwest Generation

Panelists:Matt Dooley, Alstom

Fred Lang, Exergetic Systems

Albert Lau, Reliant EnergyDick Storm, Storm Engineering

4:30 p.m. Adjourn

Thursday, February 1, 2001

7:00 a.m. Continental Breakfast

8:00 a.m. Panel: What improvements in heat rate information are needed?

Moderator: Duane Hill, Dairyland Power Cooperative

Panelists:Ron Griebenow, Performance Consulting Services

Darrell Howard, TVA

Gary Walling, Alliant Power

William Yee, Reliant Energy

9:15 a.m. Session 6: Plant Experiences

Session Chairs: Gary Walling, Alliant Energy and Jose Sanchez, EPRI

9:15 a.m. Analysis of Variables for Predicting Power Output at the Columbia Power

Plant

Aravindan Rangarajan, Industrial Engineering MS, Iowa State University

9:40 a.m. Experience of Moneypoint Power Station in Recovering Plant Heat Rate:

Focus on Control Valves

Michael Rocke, Electricity Supply Board, IrelandTom Canning, Electricity Supply Board, Ireland

Sanjay V. Sherikar, Control Components, Inc.

10:05 a.m. Break

10:35 a.m. Heat Rate Improvement in an Existing Multifuel Unit

Joaquin G. Blas, Hidroelectrica del Cantabrico, S.A.Florentino Blanco, Hidroelectrica del Cantabrico, S.A.

11:00 a.m. An Application of the Plant Performance Modelling Package PROATES to

Analyse the Cause of a Persistent Tube Failure ProblemK.R.J. Hartwell, Powergen

A.B. Ready, Powergen


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11:25a.m. In-Situ O2 Probe Failure at Dairyland‘s JP Madgett Station Duane Hill, Dairyland Power Cooperative

11:50 a.m. Closing Remarks

12:00 p.m. Adjourn


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CONTENTS

1 SESSION 1: ON-LINE HEAT RATE MONITORS ................................................................ 1-1

Real-Time Performance Monitoring of Coal-Fired Units...................................................... 1-2

Duane Hill, Dairyland Power CooperativeSastry Munukutla, Tennessee Technological University

F-Factor Method for Heat Rate Measurement and its Characteristics................................1-23

Nenad Sarunac, Lehigh UniversityCarlos E. Romero, Lehigh UniversityEdward K. Levy, Lehigh University

The Input/Loss Method......................................................................................................1-61

Fred D. Lang, Exergetic Systems, Inc.

Recent Experiences with Performance Monitoring at TVA using EPRI’s PlantMonitoring Workstation......................................................................................................1-73

Robert Inklebarger, TVAEric Sikes, TVACyrus Taft, EPRI I&C Center

Modular Heat Rate Calculations for Power Plants using OPC...........................................1-85Harry R. Winn, Westinghouse Process Control, Inc.

Improving Results, Confidence and Decisions with Precise Data Validation....................1-109

Duane Hill, Dairyland Power CooperativeMarcus Caudill, Performance Consulting Services, Inc.Ron Griebenow, Performance Consulting Services, Inc.

2 SESSION 2: OPTIMIZATION .............................................................................................. 2-1

Impacts of Combustion Optimization on Power Plant Heat Rate ........................................ 2-2

Carlos E. Romero, Lehigh UniversityEdward K. Levy, Lehigh UniversityNenad Sarunac, Lehigh University


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Obtaining Improved Boiler Efficiency and NOx using Advanced Empirical Optimizationand Individual Burner Instrumentation on a Boiler Operated in Load-Following Mode .......2-30

E. P. Payson, Allegheny Energy SupplyDave Earley, Air Monitor CorporationRich Brown, EPRI

Carlos Moreno, Ultramax CorporationApplication of GNOCIS™ Neural Network Optimization Controller for Boiler EfficiencyControl ..............................................................................................................................2-63

Darrell A. Howard, TVALonnie Coffey, EPRI I&C Center/TVA

Heat Rate Improvement at Dairyland’s Madgett Station using NeuSIGHT™ ......................2-68

Duane Hill, Dairyland Power CooperativeBrad Radl, Pegasus TechnologiesGlen Foster, Data Systems & Solutions

ProcessLink™ at the Roanoke Valley Energy Facility ........................................................2-80

Don Keisling, LG&EPeter Spinney, NeuCo

Automatically Control NOx with Heat Rate Constraints, in a Coal-Fired Power Plant ......2-107

Kandi Forte, Reliant EnergyTom Cowder, Reliant EnergyRussell F. Brown, Pavilion Technologies, Inc.

Unit Optimization at Hammond Unit 4..............................................................................2-128

John Sorge, Southern Company Services

3 SESSION 3: INTELLIGENT SOOTBLOWING..................................................................... 3-1

Effects of Sootblowing in Coal-Fired Boilers on Unit Heat Rate and Nox............................ 3-2

Carlos E. Romero, Lehigh UniversityNenad Sarunac, Lehigh UniversityEdward K. Levy, Lehigh Univerisity

Optimization of Boiler Sootblower Operation .....................................................................3-35

Jeffery Williams, Westinghouse Process Control, Inc.Xu Cheng, Westinghouse Process Control, Inc.Bernie Begley, Southern California Edison, Inc.Alex Smith, Southern California Edison, Inc.

Dale Hopkins, Southern California Edison, Inc.Intelligent Sootblowing Application Development...............................................................3-56

Neel J. Parikh, Pegasus Technologies, Inc.Brad J. Radl, Pegasus Technologies, Inc.

Intelligent Sootblowing – Boiler Cleaning Management System ........................................3-63

Randy Carter, Applied Synergistics


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An Application of the Plant Performance Modelling Package PROATES to Analysethe Cause of a Persistent Tube Failure Problem ...............................................................6-63

K.R.J. Hartwell, PowergenA.B. Ready, Powergen

In-Situ O2 Probe Failure at Dairyland’s JP Madgett Station................................................6-75Duane Hill, Dairyland Power Cooperative


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1-1

1SESSION 1: ON-LINE HEAT RATE MONITORS


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Session 1: On-Line Heat Rate Monitors

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1.9

3.5

4 .5

3 .7

0

1

2

3

4

5

Site 1 S ite 2 Site 3 S ite 4

Test Site

P e r c e n t F l o w

D i f f e r e n c e

Site 1: Short Stack, RA = 4 Deg

Site 2: Short Stack, RA = 9 Deg .

Site 3: Tall Stack, RA = 13 Deg .

Site 4: Tall Stack, RA = 6 Deg

Difference in Flue Gas Flow Rate C alculated from Standard and

Improved Instrumentation for Velocity Head M easurement

Test Instrumentation Error


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Achieved Reductions In Flow Bias Error

3.4 3.4 3.4

4. 2 4. 13. 8

10.3

13.2

18.3

13.9

9. 8

7. 1

0

2

4

6

8

10

12

14

16

18

20

Unit A Unit B Un it C Un it D Un it E Un it F

Tested Unit

R e d u c t i o n i n C E M B

i a s E r r o r [ % ]

Old EPA Regulations

New EPA Regu lations


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30

-4

-2

0

2

4

6

-250 -200 -150 -100 -50 0 50 100 150 200 250

Chan ge in Stack Temp erature [F]

E r r o r i n C o n c e n t r a t i o n M e a s u r e m e n t

[ % ]

60 Deg. F = 1% Error

Nominal Parameters

Tstack = 250 deg. F

Pstack = 1 a tm.

MW stack = 3 0 lb/lbmol

P re g = 50 psig

Tumb.cable = 80 deg. F

Dilution Probe Errors


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F-Factor Method - Field Results

Flue ga s f low rate m easured by Au toprobeTM

. Grid finene ss= 48 points

9,500

9,600

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9,900

10,000

10,100

10,200

0 5 10 15 20 25 30 35 40

Test Number

N e

t U n i t H e a t R a t e [ B T U / k W h ]

Input/Output

BTCE

F Factor, O2-Based

F Factor, CO2-Based


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Bull Run DCS GraphicsControllable Losses


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Bull Run DCS GraphicsBoiler Performance


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Bull Run DCS GraphicsFeedwater Heaters


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Modular Heat Rate Calculations for Power Plants using OPC

By:

Harry R. WinnWestinghouse Process Control, Inc.


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Modular Heat Rate Calculations for Power Plants using OPC

Or how to use OPC in power plants

as a system wide performancemonitoring tool


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Performance Monitoring

Traditional Methods...

Proprietary Code usually not supplied

• Proprietary Datalink to DCS required

• Difficult for users to make modifications

• Costly to have the performance monitoring

vendor make modifications• Eventually does not match plant

configuration due to lack of maintenance

• Difficult to get key performance data to

users on plant network


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The challenge---

• Develop performance monitoring system

which is user friendly, easy to maintain, and

provide needed data to the engineer in the

format required

• Provide an standard communication link to

all DCS vendors that is easy to configure and

maintain and allows bi-directional

communication

• Provide option to allow the performance

monitoring system to execute at the

“enterprise level” and send critical data


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The 1st Challenge---

• Develop standard method of interface

to DCS systems

• OPC was chosen as interface method


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“OPC (originally OLE for Process Control) is an industry

standard created with the collaboration of a number a leading

worldwide automation and hardware software suppliers working

in cooperation with Microsoft. The standard defines method

for exchanging realtime automation data among PC-based

clients using Microsoft operating systems.

The organization that manages this standard is the

OPC Foundation. “

OPC--- What is it?


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OPC --- Why use it?

•Most DCS vendors have OPC server

•Standard design of protocol based on OPC

specification

•Browser capability

•Easily configurable

•No custom programming•Enterprise level of performance monitoring

•www.opcfoundation.org


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Typical OPC Performance

Monitoring Configuration:

DCS Vendor #3 OPCServer



PerformanceMonitoring System for

three units

Bi-directional Data

Communication

Bi-directional Data

Communication

Bi-directional Data

Communication

EthernetEthernet

Key Performance

Data to users on

plant network

DCS PM Operator

Graphics

DCS PM Operator

Graphics

DCS PM Operato

Graphics


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The 2nd Challenge---

• Develop user friendly performance

monitoring system that is easily

configurable and maintainable

• Complete “point and click” capabilty

• Develop monitoring system for bothfossil fired utility type boilers and

combined-cycle plants


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Performance Monitoring System

User Interface

• Centralized performance monitoring

tag database

• Complete point and click capability to

configure and maintain

• Custom algorithms created forperformance monitoring modules

including condenser, boiler, turbine,

feedwater heater, etc.


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PlantPlant

attributesattributes

AlgorithmAlgorithm

selectionselection

FlowchartFlowchart

workspaceworkspace

Drop and dragDrop and drag


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Typical Condenser Calculation

Worksheet


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Condenser Design Data Screen


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OPC Tag Configuration


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Event Logger to easily track

system


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OPC tag selection output mapping


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Run mode and “What-If”

manual constant entry


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Run mode view of calculations


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Any PlantAny Plantinputsinputs

Any plantAny plant

equipmentequipment

typetype

PlantPlant

OverviewOverview

Flow ChartFlow Chart

Live valuesLive values

displayeddisplayed


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1st Installation ...

• Implemented at AES Warrior Run

project in Cumberland, Maryland

– 210 gross megawatt rating

– CE coal fired fluidized bed boiler

– ABB steam turbine– Co-generation plant

• Commissioned in May, 2000


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Controllable Loss Operator

Graphic


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Controllable Loss Operator

Graphic #2


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Cooling Tower Operator

Graphic


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Improving Results, Confidence

and Decisions

with

Precise Data Validation

Marcus Caudill

Ron Griebenow, P.E.

Performance Consulting Services, Inc .

Duane Hill

Dairyland Power


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Why Is Data Validation Important?Why Is Data Validation Important?

Instrument Errors: Reduce Efficiency

Increase Emissions

Cause Unnecessary Power Purchases

Create Monitoring System Errors

Cost $$$$$$$$$$$

– ~$1,000,000 per year on a 440 MW fossilplant


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ALLALL Automated Systems NeedAutomated Systems Need

Accurate Data for Reliable ResultsAccurate Data for Reliable Results Control System

Combustion Optimization System

Expert / Advisory System

Performance Monitoring System

On-line Costing System


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What is Advanced DataWhat is Advanced Data

Validation?Validation? Recognizes Interdependence of All Data in

Man-Made Systems

Utilizes Repeatable Patterns of ProcessData

Applies Localized (Relevant) Models to

Current Operation Provides Precise Analytical Redundancy

Provides Data Filtering for AutomatedSystems


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Data FilteringData Filtering

ACMAutomated

System

Raw

Inputs

Filtered

Values

Outputs


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Calibration Optimization /Calibration Optimization /

Calibration ReductionCalibration Reduction Early identification of instrument drift

Identify instruments requiring calibrationduring an outage

Identify instruments that DO NOT requirecalibration

Utilize highly-skilled techs in higherpriority tasks


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Duke NuclearDuke Nuclear

Oconee Nuclear Station

900 MW PWR producing SuperheatedMain Steam

ACM Capability Demonstration TrackingCalometric Data

– 44 points tracked over a 4 month period


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With reference data primarily from the

first week of operation, this temperature

is predicted within 0.1% of actual value.


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Drift in this main steam temperaturemeasurement is easily detected and

alarmed before the measurement had

drifted 4 degrees (less than 1%).


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A loss of gross generation associated

with the error in calculated steamtemperature rise, feedwater venturi

fouling and changes in condenser

backpressure is also easily detected


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Accurate replacement values

are provided during temporary

instrument faults.


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Allegheny Harrison Unit 2Allegheny Harrison Unit 2

650 MW Rated Capacity

Foster Wheeler Opposed-Wall, Coal-Fired,Supercritical Boiler

Westinghouse Single Reheat Turbine


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Allegheny ProjectAllegheny Project

Data Validation for Existing On-LinePerformance Monitoring System

Initial Data Gathering and ModelDevelopment in Early 1999

Seven-Week Turbine Overhaul and BoilerOutage Started April 1, 1999– System used to identify post outage process

changes– models updated for post outage operation


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Range of reference data (~50-

100% load, consistent with

performance monitoring

system operation)


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Very accurate modeling of main steam

temperature prior to unit outage (except shut

down periods where no reference data exists).


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Increase in top heater drain temperature.

Subsequent analysis reveals corresponding

decrease in heater level and increase in DCA.


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B-side heater shell pressure tracks

closely with prediction.


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A-side heater shell pressure clearly

drops (


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Allegheny Harrison Unit 2Allegheny Harrison Unit 2System ViewSystem View

Pre-Outage

Post-OutageEarly March Late March


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Turbine Overhaul

Notable increase in gross generationfollowing turbine overhaul.


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Turbine Overhaul

Increase in steam flow due to replacementof many turbine seals.

• increases in turbine train pressures

confirms increase in steam flow

• supports generation increase


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Turbine Overhaul

Expected improvements in heat rate due

to turbine overhaul (>1/2%) confirmedthrough data validation. Validation of all

values used in heat rate calculation

increases confidence in calculated result.


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Turbine Overhaul

No increase in HP turbine efficiency

after turbine overhaul.


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Turbine Overhaul

Substantial (unrealistic) increase in

calculated IP turbine efficiency after turbine

overhaul. Performance monitoring system

includes assumed N2 leakage, which wasgreatly reduced with seal replacement.

Calculations must be updated.


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Turbine Overhaul

After transmitter replacement, hot

reheat pressure at the boiler shows

increase consistent with increasedsteam flow (reduced turbine leakage).


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Turbine Overhaul

Hot reheat pressure at the turbine

substantially lower than expected.

Screens placed in steam path after

work in boiler reheat section

produce large pressure drop.


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Turbine Overhaul

Extensive air heater work duringoutage, including replacement of

several baskets. Clear reduction

in air heater pressure drop on

both the air and gas sides.


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Turbine Overhaul

Several transmitters changed from

absolute to gauge during outage. Will

require update to performance

calculations and data validation system.


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OPG Lambton Unit 3OPG Lambton Unit 3

Stand-alone Data Validation for PlantControl System

– Retrieves data from control system

– Can send alarms back to control system

Initial Data Gathering and ModelDevelopment in Spring 2000

Installation October 2000

Installation Being Expanded to 4 Units


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ACM detects FWH drain temperature spikes

resulting from low heater level


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During the daily automatic calibration cycle,

ACM can provide an accurate replacement

value.


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Dairyland Power JPMDairyland Power JPM

– Installing NeuSIGHT Optimization System

– Requires Extensive Data Set

– Found O2 Calibration Errors After Data SetWas Collected

– Existing ACM Models Were Used to Assess

O2 Accuracy– ACM High Accuracy Predictions Were Usedto Validate and Replace Faulty O2 Data ForTraining of the Neural Network Models


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Measured value lower than prediction until

calibration on 2/3.


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Drift begins on 2/1. No Change during 2/3

calibration.


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Measured value lower than prediction for

entire time frame.


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Matches well until 1/31, then measured value

drifts low.


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Measured value higher than prediction until

calibration on 2/3. Begins to drift again on

2/8.


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Measured value slightly higher than

prediction until calibration on 2/3, then a

good match.


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APR Data Validation Has BeenAPR Data Validation Has Been

Successfully Applied To:Successfully Applied To: Data filtering and validation for:

– Performance Monitoring Systems

– Control Systems

– Neural Network-Based Optimization Systems

Stand-Alone Data Validation

Calibration Optimization and Reduction


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ConclusionConclusion

Precise Data Validation

Improves Performance Monitoring andOptimization System Results

Increases Operations and EngineeringStaff Confidence in On-Line Information

Provides Reliable Information to SupportOperations and Maintenance Decisions


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Page 1 of 1

Improving Results, Confidence and Decisions

With Precise Data Validation Duane Hill

Dairyland Power Cooperative, Inc.

Marcus Caudill Ron Griebenow, P.E.

Performance Consulting Services, Inc.

AbstractIn the increasingly competitive electric power generation market, it is critical that allgeneration resources be utilized in the most cost-effective manner. In particular, it isessential that the operation and maintenance costs of steam power cycles be minimizedwhile maintaining peak availability, reliability, efficiency and environmental compliance.

Advanced control technologies and artificial intelligence are becoming more frequentlyused to support these optimization efforts. However, these advanced technologies areheavily reliant upon the validity of the input data.

Application of advanced data validation methods can improve the reliability of and theconfidence in intelligent control technologies. Using advanced data validation to pre-process the plant data that is used by performance monitoring, combustion optimization,plant control, and artificial intelligence systems will provide these systems with accurateand reliable information, increasing confidence in the calculated results and operationalrecommendations.

In addition, advanced data validation can accurately identify instruments requiringcalibration. Calibration efforts can be then focused on only those instruments that needattention, reducing total hours required for instrument maintenance.

Advanced data validation methods have been applied in various ways to a number ofgenerating units, including Dairyland Power Cooperative’s J.P. Madgett and GenoaStations and Allegheny Power’s Harrison Unit 2. This paper provides an overview ofvarious data validation methods and outlines some of the benefits of advanced datavalidation in reducing operation and maintenance costs. It also presents some of thespecific findings from initial analyses at these sites. Oral presentation at the EPRI HeatRate Improvement Conference will include additional case studies from various utilityinstallations illustrating the integration of advanced data validation into plant automation,monitoring, and intelligent control applications.


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Page 2 of 2

What Is Data Validation and Why Is It Important?In an attempt to improve plant performance, reduce operating and maintenance costs,

and meet the requirements of new regulation, many utilities are implementingautomated systems and new technologies for plant monitoring and control. However,the usefulness of these systems is dependent upon the reliability and accuracy of theinstrumentation. While the optimization algorithms are often mathematically “perfect” toachieve best operating conditions, the data entering these systems is usually less thanperfect. Erroneous input data can result in misleading performance assessments,inappropriate operator actions, inefficient plant operation, excessive plant emissions,and a host of other undesirable effects. This is why accurate, reliable data is critical tocost-efficient operation of any generating facility, and why implementing a cost efficientmethod of plant data validation is essential.

Note that even though questionable data supplied to a performance monitoring systemwill produce questionable results and reduce operator confidence in the system, thedata does not truly impact the integrity of the calculations. When good data is restored,

the calculated performance results will be correct. This is not necessarily true withneural-network based optimization systems. Because these systems are developed(“trained”) based on measured plant data, it is essential to provide accurate data forinitial training of the system and, possibly even more importantly, for subsequent on-linere-training, if the system has this capability. Otherwise, errors will be built into thenetwork calculations causing incorrect optimization scenarios.

Measured data obtained from process sensors and associated electronic or pneumaticequipment is the only path for the unit operator, automatic control system or computer-based operator support system to obtain process information. Therefore, it is veryimportant that the quality of process information be high. While instrument reliabilityand quality has improved significantly in recent years, wholesale instrumentreplacement is difficult to justify and usually not warranted. Erroneous data fromsensors must be quickly and effectively identified so that the operator and anyautomated analysis or control functions can maintain reliable, efficient operation. Invalid

sensor signals may result in misleading or incorrect conclusions and inappropriateresponses (e.g., an invalid measurement could be mistaken for a process fault) by anoperator or computer-based system, possibly producing unnecessary processshutdowns or equipment damage.

Historically, instrument calibrations have been performed on a time basis, with thoseinstruments that were deemed as critical to safety and efficient operations calibratedmore frequently than those viewed as less important. This method of calibration hasseveral side effects. First, the calibration intervals for the critical instruments may beclose enough together that the instruments would not change significantly, so while theintegrity of the instrument signals was maintained, the instrument maintenance staffspent precious resources calibrating instruments that did not need calibration. Second,due to the time required to maintain these critical instruments, the less importantinstrument calibration intervals tend to get longer and longer until a problem with one of

these less important systems becomes critical.


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Page 3 of 3

In short, quality data is essential to get effective results from and use of automated

controls, computerized optimization systems and operator response. Data validationprovides information to help distinguish measurement failures from process faults andselect which instrument signals to use in control and analysis functions. High qualitydata validation can also reduce the time spent on calibration of instruments that arewithin specifications, and can identify those instruments that are beginning to change,so that a calibration check can be scheduled.

Data Validation MethodsData validation has been an issue for power producers as long as there have beenpower plants. In many cases, the traditional form of data validation was to take a “testinstrument” out into the plant and install it adjacent to a questionable instrument andcompare the two. If the test and plant instrument were close enough, then the plant datawas considered valid. As fuel cost became more of an issue in the industry and

additional automation required additional instrumentation, more complex and morecostly data validation schemes emerged. Many of these methods of data validationresulted in significantly higher instrument maintenance costs and provided onlymarginally better instrument accuracy and reliability. The more common methods ofmaintaining instrument accuracy are presented below, along with a discussion ofadvanced data validation.

Blanket CalibrationThe data validation method that has been most commonly used throughout the industryis blanket calibration. Historically, power generators have scheduled unit maintenanceon a time basis (12 month intervals, 18 month intervals, etc.) and during thesemaintenance outages, many of the process instruments were scheduled for calibration.When preparing for these outages, it was not unusual for the plant staff to budget 500 to1500 hours or more for instrument calibrations. In addition to the staff time, high-qualitycalibration equipment is required for each technician performing the calibrations. In

many cases, technicians found that they were calibrating instruments that did not needcalibration and, because of the length of time between calibrations, they were notdeveloping useful calibration history on the individual instruments. For example, theymight record that a particular pressure transmitter was out of calibration three years in arow, but they had no idea when during that year the calibration drift occurred. Betweenthe intensive manpower requirements and the calibration history developed, there aredefinite drawbacks to blanket calibrations.

Data ComparisonComparison methods are based on the availability of at least two measurements (director derived) for a desired process state. These “redundant” indications may then be usedto make some judgment about the validity of the measurement signals. The simplestcomparison methods involve the installation of two sensors at the same location for thesame process state. These redundant sensors are compared with each other, and

disagreement between them, larger than a threshold (which is typically related to the


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Page 4 of 4

amount of anticipated measurement noise), is considered indicative of failure of one ofthe measurements. However, if only two measurements are available, no decision can

be made on which to accept. An unambiguous measurement quality determinationrequires more than two measurements. When at least three measurements areavailable for comparison, it is possible to make some logical choice of which to acceptor reject, and to form a "best estimate" of the true value of the process state.

When there are no physically redundant devices, it is possible to use analyticallyredundant measurements. An analytically redundant measurement is most oftenderived using a process model to calculate a representative value of a directlymeasured state from measurements of other states. Once three or morerepresentations of a particular measurement are available, there are methods whichmay be used to discriminate failed measurements and select the most representative"true" value of the measurement.

Advanced Data Validation

The usefulness of the methods described above has been severely limited by the costand complexity of their implementation, as well as the accuracy and reliability of theirresults. Blanket calibrations require a significant investment in highly trained staff and acontinuous block of dedicated time for maintaining calibrations. Installing andmaintaining redundant sensors is costly and tends to be limited to a small subset of thetotal number of sensors that impact process performance and reliability. Comparisonmethods also generally compare only two or three measurements to each other or ameasurement to a fixed limit or a simple single-variant curve. Consequently, thisapproach is not very precise or robust, and is therefore unlikely to cover a large subsetof process measurements. These limitations have deterred many utilities from initiatinga comprehensive data validation program. Fortunately, advanced data validationtechnology resolves many of the shortcomings of these traditional methods.

Advanced data validation works in much the same way as the human brain. The humancontrol operator with an analog control system observes the positions of dials, gauges

and other indicators on the control panel and compares the current “picture” of unitoperation with previous “pictures” that are stored in memory to decide if the unit isoperating in an acceptable manner. If the “picture” matches something that the operatorremembers, either from training or from past operation, then the operator can identifythe current operation. If something in the “picture” deviates from past experience, theoperator might use part of the picture to make a prediction (“that pressure should be200 psi”).

The advanced data validation technology works in a similar way. Snapshots of historicaldata are collected, evaluated to assure that all of the information within the snapshot isgood, then included in a reference data file. Then as new data is evaluated, a snapshotof current data is compared to the reference snapshots, and the system determines ifthe current operation is consistent with past operation. A detailed explanation of themethodology can be obtained in Reference 1.


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Page 5 of 5

The advanced data validation approach brings several advantages over other numericalmethods. For example, since this technology recognizes that all monitored parameters

are interrelated, the underlying algorithm is highly fault tolerant; the effects of incorrector missing plant measurements are minimized.

It is important to realize that advanced data validation is not a replacement forconventional performance monitoring, combustion optimization or other plant processimprovement system. It is designed to work in conjunction with these systems, providinghigh quality data so that these conventional systems can achieve optimum results.

Advanced Data Validation ApplicationsAdvanced data validation systems have been or are being installed at a number ofgenerating stations, both in the US and overseas. Personnel at these sites espouse awide range of performance monitoring philosophies and reasons for installing datavalidation systems. However, all realize that the common problems with data reliability

that exist in all power plants will prevent them from achieving their lowest cost ofgeneration. Each of the examples below present the utility’s motivation for installingdata validation, their goals for the installation, and examples of the results achieved todate.

Dairyland ApplicationDairyland Power Cooperative is a long-time user of on-line performance monitoring andhas a history of being on the cutting edge of performance improvement technology.After deciding to upgrade from a VAX-based performance monitoring system to aWindows NT-based system, Dairyland staff recognized the need to verify data quality.The data quality issue was to be even more important since Dairyland had plans toinstall a neural network-based optimization system. Dairyland knew that both theperformance monitoring system and the optimization system were good programs but,to work effectively, they need good data.

Another primary motivation for installing a data validation system at Dairyland was toreduce the load on instrument technicians. The three technicians at JP Madgett stationwere already overloaded, and each time that blanket calibrations were performed,Dairyland technicians were finding a number of instruments were scheduled forcalibration when they didn’t need to be. What was really needed was a tool to assist theinstrument technicians in determining when an instrument needed calibration orreplacement. To this end, advanced data validation was implemented to monitorapproximately 260 instruments.

Dairyland’s performance improvement program relies heavily on the existingperformance monitoring system, which is used not only for performance calculations,but also as a data archival and retrieval system. In addition, the optimization systembeing installed will acquire its data through the performance monitoring system. Basedon these considerations, Dairyland decided to integrate the data validation system with


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Page 6 of 6

their existing performance monitoring system. Figure 1 depicts the data flow of theDairyland system.

In one of many examples of the system’s benefits, it identified an anomaly in the high-pressure feedwater heater drain temperature. Since this was happening at night at lowloads, the problem most likely would not have been identified through existingmonitoring activities. The controller was set improperly causing a drain valve to openwhen it was supposed to be closed. Correcting the problem allowed the heater toperform much more effectively, improving unit overall efficiency.

Figure 1 -- Integration of Advanced Data Validation at Dairlyand’s JP Madgett

In another example, the performance staff noticed that one of the two Madgettfeedwater flow transmitters had failed. The operations staff had noticed the same thingand were trying to determine the time of failure. Using the performance monitoringsystem, a trend of both flow transmitters and the advanced data validation systempredicted flow value was displayed. The trend, depicted in Figure 2, shows that onetransmitter began to “straight line”, indicating the time of failure. Further, the predictedvalue provided an accurate replacement for this failed transmitter that could be used forcontinued operation until the failed instrument in replaced.

PC Windows NT 4.0

Unit ControlSystem

AdvancedData

ValidationProcess

PerformanceMonitoringCalculations

Perf. Monitor DatabaseDAS Value StorageACM Value StorageCalc Value Storage

DASValue Validated

DAS Value

DAS orReplacementValue

CalculatedResults

Neural Network-basedOptimization System

(Planned)


OptimizationInstructions


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Page 7 of 7

In yet another example, the plant had been experiencing unusual operating problemsfor several days. Operators reported that the unit was “operating strangely at lowerloads”. A review of the advanced data validation system results revealed that the WestPrimary AirFlow transmitter was fluctuating between 200 KPH and its maximum value of531 KPH. The advanced data validation system prediction did not show this fluctuation,indicating that there was a problem with the transmitter. Shortly after this problem wasdiscovered, the transmitter failed completely. This illustrates the value of data validationin pinpointing the root cause of operating problems. Identifying the deficient instrumentsaved Dairyland staff a significant amount of diagnostic time.

Finally, following weeks of parametric testing for the implementation of a neuralnetwork-based combustion optimization system, performance engineering staffdetermined that the boiler oxygen measurement probes had been experiencingplugging resulting in an erroneously low indication of excess air. Gradual drift in theboiler oxygen measurements was confirmed by reviewing the advanced data validationsystem results. The data validation system predicted values were subsequently usedfor successful training of the neural network saving weeks of repeated testing.

Based on the success of their current installation, Dairyland is looking to use the systemto provide early identification of performance degradation, in addition to expanding theinstallation to additional units.

Allegheny Energy Harrison Station ApplicationAllegheny Energy realized that in order to remain competitive in a deregulatedenvironment that new approaches to traditional business practices were in order. WhileAllegheny historically has been proactive in the performance improvement area,personnel reduction and turnover has resulted in rethinking performance improvementmethods and turning to computerized performance tools. Two of the tools that wereimplemented early in the process were a standardized information database and a

standardized performance monitoring system.

Figure 2 -- Dairyland Feedwater Flow Transmitter Failure

500

1000

1500

2000

2500

3000

Time

FW

FLO

w

Actual FW Flow 1 Actual FW Flow 2 ACM Predicted FW Flow 1


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Implementation of a information database provides Allegheny staff with instant access

to a wealth of plant process data, both at the plant and at the corporate level. With theincorporation of performance calculations, operations, maintenance and engineeringstaff not only have access to data, but to calculated performance results such as turbineefficiency, boiler efficiency, and unit heat rate. While all of this information istremendously useful, it also points out a weakness that is common to all automatedsystems: the results are only as good as the incoming plant data. Over time,transmitters and other process indicators can drift and there is simply not enoughmanpower available to continuously check the calibration of critical instruments. Withincreased emphasis on heat rate and performance, Allegheny staff realized thatproperly calibrated instrumentation is a key to being able to control the generationprocess and maintain peak efficiency. Advanced data validation provides an economicaland efficient way to monitor the calibration of important sensors.

At Harrison Station, all historical data is stored in the OSI-PI data base on a DEC Alpha

with an Open VMS operating system. The existing performance monitoring system,which also resides on the DEC Alpha, retrieves data from the PI database, runs theperformance calculations, and sends results back to the PI system. Since the initialapplication of data validation was intended to improve confidence in the performancecalculations, there were a number of options for integration with this system. First, thedata validation system could directly filter data from the plant control system. Second, itcould communicate with the PI system and provide validation information back to thedatabase. Third, the data validation system could be interfaced directly to theperformance monitoring system, validating only the information used for theperformance calculations.

After considering all of the possibilities, Allegheny opted to configure the system so thatit communicated with the PI database. Since OSI PI is the standard databasethroughout the Allegheny system, this interface would allow use of the same interfacesoftware on all of the Allegheny units. In addition, all data from the plant computers are

stored in the PI system, allowing validation of all plant data, whereas only a limitednumber of process points are transferred to the performance monitoring system. Figure3 depicts the integration of the advanced data validation system with the plant databaseand the performance monitoring system.

Data validation models were developed for Harrison Station Unit 2 in the spring andsummer of 1999. Installation of the system was scheduled to follow a seven-weekboiler outage and turbine overhaul that began April 1, 1999. In addition to identifyingseveral data problems prior to the outage, the system was used to identify changes inthe system following the outage.


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Page 9 of 9

Figure 3 -- Integration of Advanced Data Validation at Allegheny’s HarrisonStation

System level displays available from the data validation system easily identifiedsubstantial process change resulting from the turbine overhaul. Figure 4 shows“bullseye plots” from both before and after the outage. Each “hole” in these plotsrepresents one of the 249 measurements or performance monitoring system results thatwere analyzed with the data validation tool. Each ring of the bullseye represents onestandard deviation between the measured value and the data validation systempredicted value.

Figure 4 -- Allegheny Harrison Unit 2 System Level Displays

Alpha – OpenVMS

Unit ControlSystem

Intel NT PCAdvanced

DataValidationProcess

PerformanceMonitoringProcess

OSI PI SystemDAS Value StorageACM Value StorageCalc Value Storage

DASValue Validated

DAS Value


CalculatedResults


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Page 10 of 10

In addition to several post-outage findings, the system clearly confirmed an increase insteam flow (Figure 5) and turbine train pressures resulting from replacement of worn

turbine seals along with a corresponding increase in generation (Figure 6) and decreasein unit heat rate (Figure 7).

Figure 5 – Harrison Unit 2 Steam Flow

Figure 6– Harrison Unit 2 Gross Generation

Turbine Overhaul

Measured ValuePredicted ValueDynamic Alarm Limits

Turbine Overhaul



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Page 11 of 11

Figure 7– Harrison Unit 2 Net Unit Heat Rate

The system was installed in late-September 1999 and continues to provide real-timevalidation for the on-line performance monitoring system.

Other Advanced Data Validation Applications

In the past, the unit operator could easily make adjustments for questionable plant data.However, with the large quantity of data that is frequently used in advanced controlalgorithms, it is impossible for an individual operator evaluate all of the input datawithout assistance.

Many facilities are implementing neural network-based optimization systems, which aredesigned to optimize many different aspects of plant operation, including NOx, heat rate,SO2, and overall operating costs. Since these systems are configured based on actualplant data from parametric testing, it is critical that high-quality data is used forconfiguration of the systems and continued operation. Advanced data validation can beintegrated into an optimization program in a number of ways. It can be configured towork directly with the optimization software and validate only the information that isbeing assessed for optimization, or it can be interfaced with the plant data acquisition topre-process the data supplied to the optimization system. At a minimum, advanced datavalidation technology should be employed to filter the data used in configuring a neuralnetwork. This will reduce the chance of incorporating faulty data into the neural networkmodel and reduce the need for costly and time-consuming retraining. Further,

Turbine Overhaul



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Page 12 of 12

implementing such a system in a “closed-loop” control mode requires high-quality, real-time data, making on-line advanced data validation essential.

Economics of Advanced Data ValidationIn the current power generation market, most business decisions are driven byeconomics. If there is not a definitive cost benefit to improving data quality, then theprudent businessperson would not install a data validation system. In addition, manyutilities are implementing requirements for a one or two-year payback period. Advanceddata validation methodology can often provide full return in less than one year throughthe following benefits.

• Increases operator confidence in data• Aids management in making accurate O&M decisions• Reduces calibration resources by optimizing calibration scheduling• Reduces engineering analysis resources by identifying the root cause of a

problem (instrument or equipment)• Provides early warning of instrument drift or failure• Provides accurate replacement values for drifted or failed instruments• Provides early warning of process drift or change• Precisely quantifies the amount of instrument/process drift or change

While all of these are important benefits to power generators, only those which can beassigned a dollar value can be used for cost justification. Examples of the cost benefitsavailable from advanced data validation are presented below.

Advanced data validation will help to reduce costs by streamlining the calibrationprocess. Utilities estimate that as many as 1500 man-hours are expended on instrumentcalibrations during annual outages. If advanced data validation can reduce this by aconservative 25%, an additional 375 man-hours would be available for more productive

tasks, such as controls tuning and optimization. Assuming that an instrument technicianwith a loaded cost of $35 per hour is performing the calibrations, a reduction of 375hours per unit results is a direct labor savings of more than $13,000 annually.

For the past 25 years the power generation industry has been on a quest to improvefuel efficiency and reduce heat rate. One of the cornerstones of this quest has been thereduction of controllable losses. A tremendous amount of research, time, training,money and effort has gone into the effort to reduce controllable losses. While this efforthas provided exceptional payback, the weak link is again the primary instrumentation.The pressure and temperature sensors must provide an accurate indication of the trueprocess value in order to minimize controllable losses. In a study performed on a single450 Mw coal-fired generating unit, the impact of historical deviations in just three of theinstruments that impact controllable losses -- throttle pressure, throttle temperature, andhot reheat temperature - was calculated to be more than $900,000 annually in additionalfuel consumption and replacement power. Since these instruments are part of the high-

profile set of controllable loss instruments, it is assumed that higher priority was placed


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Page 13 of 13

on maintenance of these instruments. With an advanced data validation system, thesecalibration deviations would be readily detected. Add these tangible savings to the less

tangible, such as increased operator confidence in the input data, and it is clear thatdata validation provides high returns.

ConclusionsAdvanced data validation has been successfully applied to pre-processing of the plantdata that is used by performance monitoring and control systems and provides thesesystems with accurate and reliable input data. This provides increased confidence inmonitoring and optimization system recommendations and reductions in plant operatingcosts. In addition, advanced data validation has been used to accurately identifyinstruments requiring calibration, refocusing cali

12th heat rate performance improvement conference

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