ADVANCED SOFTWARE TOOLS FOR OPTIMIZATION OF POWER PLANT PERFORMANCE

Transcription

1 ADVANCED SOFTWARE TOOLS FOR OPTIMIZATION OF POWER PLANT PERFORMANCE ARC Tenth India Forum, Hyderabad 5-7 July, 2012 Transforming Industry and Infrastructure through New Processes and Technologies Debasish Patra Addl. General Manager Steag Energy Services (India) Pvt Ltd 1

2 How to Improve Efficiency? First Step: low hanging fruits Improve efficiency by improving operation and maintenance Next Steps: Audit or map the plant using off line modelling tool e.g. Ebsilon Online optimization tool PADO Online fault detection system using SPC and Fault trees Online life time monitoring SR1 for better planning of inspections and maintenance 2

3 OFFLINE TOOL Ebsilon 3

4 Offline Tools - Applications Power Plant Design for new Power Plant Mapping and Performance Analysis of existing Power Plants Planning for Renovations 4

5 Modeling Technique Topology, design-parameters, measured values Data collection Topology, design-parameters, measured values Feeding into software Stationary simulation of certain scenarios Analysis and reporting 5

6 Features of the calculation program The software that shall be used for this demo is called Ebsilon Professional (it stands for Energy Balance and Simulation in Open Networks ) It has a history of approximately 20 years It is used by more than 120 companies (manufacturers, utility companies, engineering companies) and 20 universities worldwide In more than 20 countries Altogether more than 700 licenses sold Up to now approximately 45 online-systems running in power-plants The software is under continuous development 6

7 Features of the calculation program User friendliness by intuitive handling (100 % Windows compliant) No programming skills required Graphical objects for components and pipes (component library and fluid library) Complete observance of physical laws (mass-balance, energy balance, ) Design and off-design calculation possible Extension by self-defined macros and by DLLs possible Large number of different fluids Fast diagnosis of topology and specification errors High stability of convergence of solution Different unit systems 7

8 Features of the calculation program With the calculation tool the following thermal power cycles can be mapped : Fossile power plants (hard coal fired, lignite fired, oil fired, gas turbine) Nuclear power plants (NPP) Combined cycle power plants (CCPP) Combined heat and power plants (CHP) Regenerative energy (Solar thermal, Geothermal, Biomass ) Additional working fluids (ORC, certain binary mixtures) Desalination processes (MFD) Refrigeration cycles 8

9 Components available for 9

10 Steps in power-plant modeling (1) Create the topology (from component library and fluid library) Specify the design values (values of the 100 % plant condition at default ambient condition) Do a design calculation (simulate the 100 % plant condition) 10

11 Steps in power-plant modeling (2) Identify components (i.e. adjust nominal values and part load performance characteristics of components from default values to real values. Derive the real values from manufacturers design data, heat-balance diagrams or from measurements) Do off-design calculation (i.e. calculate values for different loadcases) Do What-If scenarios (i.e. investigate the behaviour of the model under different conditions) 11

12 Modeling power-plant Study case: Detailed mapping of Water Steam Cycle of a 210 MW power plant 12

13 ONLINE TOOL PADO 13

14 Signing of Framework Agreement 14

15 PADO in India 72 units order for PADO have been placed on STEAG mostly through its framework agreement with BHEL 23 units successfully commissioned till date National Thermal Power Corporation (NTPC) has standardised specification based on STEAG supplied PADO for all future units including Super-Critical Units. Bharat Heavy Electricals Limited (BHEL) The largest supplier of power equipment with 70% of current installed market of Thermal Power Plants has a Framework Agreement with STEAG for installation of PADO. 15

16 PADO in India (2) 49 units where PADO is commissioned or under commissioning NTPC Simhadri 2x500 MW NTPC Ramagundam 1x500 MW NTPC Rihand 2x500 MW NTPC Talcher 4x500 MW NTPC Kahalgaon 3x500 MW NTPC Sipat 2x500 MW NTPC Vindhyachal 2x500 MW NTPC Korba 1x500 MW NTPC Dadri 2x500 MW NTPC Farakka 1x500 MW Mahagenco Khaparkheda 1x500 MW Mahagenco Bhusawal 2x500 MW NTPC Simhadri (stage II) 2x500 MW NTPC Jhajjar 3x500 MW KPCL Bellary (KPCL) 1x500 MW RVUNL Stage 1 and 2 Chhabra 3 x 250 MW Shree cement Ltd. RAS DVC Maithan 2x500 MW GEB Ukai 2x500 MW NTPC Korba Extn 1x500 MW NTPC Bongaigaon 3x250 MW TNEB North Chennai 2x600 MW CSEB Marwa 2x500 MW CSEB Korba 1x500 MW L&T Rajpura 2x700 MW 16

17 Advantages of PADO Improving the quality of measurements by data validation Evaluation of boiler, turbines, condenser and other components Optimization of unit operation (sootblowing, setpoints) Calculation of what-if scenarios Generation of daily and monthly reports Enhance the efficiency of the power plant! 17

18 Modules of PADO System Boiler Fault Soot Blowing Setpoint Optimizer Statistical Process Tree Optimizer Control Data Visualizer Data Validation Data Management System Ebsilon Model Base Modules Fault Detection Optimization Physical Condition Lifetime Monitoring Metal Temperature Performance Monitoring What-If Analysis Performance Analysis 18

19 SR::x Data Management System SR::x is the central data management in the SR product family featuring: state-of-the-art visualization Long-term storage of measured and computed values in time-oriented archives; Automatic aggregation of data to higher time classes such as 5, quarterly, hourly, daily, monthly- or yearly- values Integrated mathematical formula editor Excel-Add-In and HTML-List Generator allow the generation of extensive reporting systems 19

20 SR::x Data Management System SR::x is the central data management with state-of-the-art visualization 20

21 Data Validation System Data Validation to replace data errors due to defective sensor or cable problems: Incorrect Data Wrong results No data No results Validation A 3 tier Process 1) Plausibility Check using Neural Networks 2) Plausibility Check based on range of Values 3) Data validation / reconciliation based on First Principle Thermodynamic model 21

22 Data Validation System (1) based on Neural Network 22

23 Data Validation System (2) based on First Principle Thermodynamics 23

24 Data Validation System (3) check the quality of measurements 24

25 Data Validation Report 25

26 Performance Monitoring Compares the actual values of critical parameters with the best achievable under current operating conditions. Shows monetary loss against each sub optimal operating parameter, defining the scope of improvement. 26

27 Turbine Performance Monitoring 27

28 Criteria for Soot Blow Optimization Fouling/Slagging: Results from thermodynamic model - SR::EPOS Time intervals - Minimum frequency of soot blowing - Minimum-pauses Configurable priorities for each criterion Soot-blowing costs Process-engineering criteria, plant-specific - Reheat spray flow - Flue gas temperature before air preheater - Furnace exit gas temperature - RH Metal Temperature - Mills Combination - Coal mass flow - NOx-emissions 28

29 Soot Blowing Optimization 29

30 Set point Optimization Shows the optimal against current set points and improvement in heat rate. 30

31 Metal temperature Module Shows the temperature profile of individual tubes of various heating surfaces of boiler and identifies the Hot spots. 31

32 Lifetime monitoring module SR1 module aims to calculate the remaining life of thick walled components in boiler the consumed life of an equipment could be different from the actual age of the equipment 32

33 Statistical Process Control Fault Prediction in Power Plant using statistical methods to corelate deterioration of key performance indicators. Air ingress false alarms plausible alarm air ingress plausible alarm air ingress 33

34 Key Performance Indicators Key Performance Indicators (KPIs) can be used to detect component failures and to enable condition based maintainance. KPI should depend only on component condition but not on load, ambient conditions or operation mode Measured vibration bearing temperature oil temperature power consumption calculated heatrate Component quality factor efficiency protection limit Key measurements in power plants usually depend on - load, - operation mode - fuel quality - ambient conditions and are superposed by noise 34

35 Fault Trees Models and analyzes faults in the process. Composed of logic diagrams that display the state of the system and the states of the components Constructed using Drag & Drop technique Does not need programming expertise for building such trees. 35

36 Other Tools for Optimization in STEAG PADO helps to optimize process parameters so that plant operates at optimum level of efficiency STEAG also offer other two software systems for plant optimization: Simulator- to improve the skills of the operators by giving training on Simulator SI- Computer based Maintenance Management system to improve the maintenance activity 36

37 Simulator 37

38 SI 38

39 Thank You for your attention!! For live demo or more questions on all the above software solutions, kindly visit STEAG Stall 39