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