added value applications ~ enabled through pi jerry weber, thermal performance specialist frank...

ADDED VALUE APPLICATIONSADDED VALUE APPLICATIONS~~

ENABLED ENABLED THROUGHTHROUGH

PIPIJerry Weber, Thermal Performance Specialist

Frank Borth, Performance & Data Engineer

Wayne Benedeck, Thermal Performance Specialist

ComEd

Changes in Generation in the Utility Changes in Generation in the Utility BusinessBusiness

Changes in Generation in the Utility Changes in Generation in the Utility BusinessBusiness

BEFORE COMPETITION• Monopoly business• Staff for All Needs• Cost-plus Pricing• Make Capital Investments

to Maintain Plant• Availability at All Costs• Produce MWh’s

UNDER COMPETITION• Competitive market• Minimum Operating Staff• Market-based Pricing• Make Capital Investments

Based upon Return on Investment

• ‘Commercial Availability’• Market Energy AND

Supplementary Services

Utilize

INFORMATION TECHNOLOGY

• to Enhance Operational Capabilities• to Better Manage Assets• to Better Serve ‘Customers’

How Will Utilities Meet the Challenge How Will Utilities Meet the Challenge of Deregulation and Competitionof Deregulation and Competition

How Will Utilities Meet the Challenge How Will Utilities Meet the Challenge of Deregulation and Competitionof Deregulation and Competition

Unit 6 DCS Unit 7 DCS Unit 8 DCS

WestationJOPN16

API NodeJOPN78

PI ServerJOPH01

PMAX & FactNetNode JOPX01

Client PC’s

Station LAN - Company WAN

ComEdComEdTypical FossilTypical Fossil

GeneratingGeneratingStationStation

BusinessBusinessInformationInformation

SystemSystemLayoutLayout

ComEdComEdTypical FossilTypical Fossil

GeneratingGeneratingStationStation

BusinessBusinessInformationInformation

SystemSystemLayoutLayout

Client ApplicationsClient Applications

• Adhoc Applications– PI ProcessBook– PI DataLink in Excel

• Prepared Applications– DataMine Performance Analysis– Generation Commercial Management (GCM)

– Operations Information (OI) using PMAX– FactNet Sensor Analysis

Thermal Performance AnalysisThermal Performance AnalysisUsing PMAX, PI and ExcelUsing PMAX, PI and Excel

Thermal ModelingThermal Modeling

• Utilize PMAX to construct thermal models of all fossil units

• Perform efficiency calculations– Unit cost of generation– Turbine heat rate– Boiler efficiency– Net unit heat rate– Controllable costs

Controllable CostsControllable Costs

• Throttle Temperature and Pressure• Hot RH Temperature• RH Pressure Drop• RH Attemperation Flow• Turbine Condition• Condenser Performance• Feedwater Heater Cycle• Auxiliary Power

Controllable CostsControllable Costs

• Station Heating• Steam and Water Losses• Sootblowing Flow• Air preheating Flow• Blowdown• Boiler Excess Air• Excess Stack Temperature• Coal Moisture• Unburned Carbon in Ash

Historical Data is ValuableHistorical Data is Valuable

• Determine largest cost drivers– Utilize Excel with a PI add-in tool– Generate scatter plots

• Help determine root causes

• Make recommendations to plant– Process Improvements – Capital Improvements

Heat Rate Deviation Heat Rate Deviation ReconciliationReconciliation

• Construct Scatter Plots for ALL Controllable Costs

• Reconciliation at 300 MW– Condenser Performance 0-150 BTU/KWh– Steam & Water Loss 0-100 BTU/KWh– Sootblowing 0-160 BTU/KWh– Throttle Temperature 0-40 BTU/KWh– Stack Loss 0-30 BTU/KWh

Data Validation andData Validation and MOREMORE

• Cost Monitoring Input Data Must Be Validated (PMAX Inputs)

• Selected Monitor Outputs Also Need Validation• Sensor and Process Deviations Must Be Identified

and Timely Corrective Action Taken• Knowledge of Proper System Operation MUST Be

Leveraged• Automated Monitoring and Data Reduction Allows

Station Staff to do MORE with LESS

Pattern RecognitionPattern RecognitionSoftwareSoftware

• Tool Chosen:

– FactNet from Pacific Simulation

• Multi-year Development History• Excellent Model Construction User

Interface• Flexible PI Interface Capabilities• Proven Implementation

Capabilities in Process Industry

FactNet ModelingFactNet ModelingDIVIDE AND CONQUERDIVIDE AND CONQUER

• Generating Units have 600-2000 Analog Points

• Separate Whole Unit into System/Sub-System Blocks

• Individual Blocks May Have UP TO 127 Sensed Data Points

• Historical Data for Model Construction – Sampled Evenly

– Over Operating Range

– Encompass 1 Year

– Up to 16,000 Snapshots

FactNet Modeling FactNet Modeling Building a Process ModelBuilding a Process Model

• Input-Output Identification

• Dataset Conditioning– Min / Max– Suspect Data

FactNet Modeling FactNet Modeling Building a Process ModelBuilding a Process Model

• Correlation Matrix• Factor Extraction

FactNet Modeling FactNet Modeling Model ValidationModel Validation

• Factor Strength Inspection

• Factor Network• Simulator

FactNet Modeling FactNet Modeling Model Testing / ProductionModel Testing / Production

• Run Model Interactively– Correct Operation– Expected Results

• Add Model to FactNet Production Server

• Meet with Station for Rollout

FactNet ModelingFactNet ModelingCurrent End User ToolsetCurrent End User Toolset

• On-Demand Reporting in MS Excel

• Configurable Timespan

• Worksheet Tabs for Each System Model

• Drives Sensor Maintenance

FactNet ModelingFactNet ModelingPreliminary ResultsPreliminary Results

• Model Implementation Identifies Vibration Problem on Turbine Generator

• Condenser Performance Issue Confirmed and Effect Minimized

• Sensor Dropouts Identified and Maintenance Implemented

• Abnormal Process Values Highlighted and Investigation In-Progress

FactNet ModelingFactNet ModelingFuture DirectionsFuture Directions

• Improved Input Validation for PMAX Performance Monitor Cost Outputs

• NOx Modeling and Reduction Activity Recommendations

• Boiler Cleanliness Modeling

Thanks from Jerry and FrankThanks from Jerry and Frank

QuestionsQuestions