Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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APPLICATION OF MONITORING, DIAGNOSIS, AND PROGNOSIS IN THERMAL PERFORMANCE ANALYSIS FOR NUCLEAR POWER PLANTS

  • Received : 2014.10.30
  • Published : 2014.12.25
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Abstract

As condition-based maintenance (CBM) has risen as a new trend, there has been an active movement to apply information technology for effective implementation of CBM in power plants. This motivation is widespread in operations and maintenance, including monitoring, diagnosis, prognosis, and decision-making on asset management. Thermal efficiency analysis in nuclear power plants (NPPs) is a longstanding concern being updated with new methodologies in an advanced IT environment. It is also a prominent way to differentiate competitiveness in terms of operations and maintenance costs. Although thermal performance tests implemented using industrial codes and standards can provide officially trustworthy results, they are essentially resource-consuming and maybe even a hind-sighted technique rather than a foresighted one, considering their periodicity. Therefore, if more accurate performance monitoring can be achieved using advanced data analysis techniques, we can expect more optimized operations and maintenance. This paper proposes a framework and describes associated methodologies for in-situ thermal performance analysis, which differs from conventional performance monitoring. The methodologies are effective for monitoring, diagnosis, and prognosis in pursuit of CBM. Our enabling techniques cover the intelligent removal of random and systematic errors, deviation detection between a best condition and a currently measured condition, degradation diagnosis using a structured knowledge base, and prognosis for decision-making about maintenance tasks. We also discuss how our new methods can be incorporated with existing performance tests. We provide guidance and directions for developers and end-users interested in in-situ thermal performance management, particularly in NPPs with large steam turbines.

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References

  1. ASME PTC PM-1993, "Performance Monitoring Guidelines for Steam Power Plants", American Society of Mechanical Engineers, 1993.
  2. ASME PTC 6-2004, "Performance Monitoring Guidelines for Steam Turbine", American Society of Mechanical Engineers, 2004.
  3. ASME PTC 6S-1988, "Procedures for Routine Performance Test of Steam Turbines", American Society of Mechanical Engineers, 1988.
  4. R. Gay, "Power Plant Performance Monitoring", 1st ed., R-Squared Publishing, 2004.
  5. R. Isermann, "Supervision, Fault-detection and Fault-Diagnosis Methods - An Introduction," Control Engineering Practice, Vol. 5, Issue 5, pp. 639-652, 1997. https://doi.org/10.1016/S0967-0661(97)00046-4
  6. SCIENTECH, "PEPSE", Retrieved on July 24, 2014, http://scientech.cwfc.com/software /spokes /06_PEPSE.htm
  7. GE Measurement & Control, "GateCycle Software", Retrieved on July 24, 2014, http://www.ge-mcs.com/en/bently-nevada-software/320-performance/1831-siwebpl655.html
  8. GP strategies, "Virtual Plant", Retrieved on July 24, 2014, http://www.etapro.com /platform /virtualPlant.aspx
  9. Thermoflow, INC., "Thermoflow" Retrieved on July 24, 2014, http://www.thermoflow.com /index.html
  10. ProSim Software & Services in Process Simulation, "ProSim", Retrieved on July 24, 2014, http://www.prosim.net/fr/index.php
  11. Engineered Software, INC., "CONVAL", Retrieved on July 24, 2014, http://eng-software. com/ products/conval/
  12. Sim Tech, "IPSE pro", Retrieved on July 24, 2014, http://www.ipsepro.com /IPSEpro /english /IPSEpro.php
  13. ASME PTC 19.1-2005, Performance Monitoring Guidelines for Test Uncertainty, American Society of Mechanical Engineers, 2005.
  14. Man Gyun Na, Sun Ho Shin, and Dong Won Jung, "Design of a Software Sensor for Feedwater Flow Measurement Using a Fuzzy Inference System," Nuclear Technology, Vol. 150, No. 3, pp. 293-302, June 2005.
  15. Man Gyun Na, In Joon Hwang, and Yoon Joon Lee, "Inferential Sensing and Monitoring for Feedwater Flowrate in Pressurized Water Reactors," IEEE Trans. Nucl. Sci., Vol. 53, No. 4, pp. 2335-2342, Aug. 2006 https://doi.org/10.1109/TNS.2006.878159
  16. Dong Hyuk Lim, Sung Han Lee, and Man Gyun Na, "Smart Soft-Sensing for the Feedwater Flowrate at PWRs Using a GMDH Algorithm," IEEE Trans. Nucl. Sci., Vol. 57, No. 1, pp. 340-347, Feb. 2010. https://doi.org/10.1109/TNS.2009.2035121
  17. G. Heo, S. Chang, "Algebraic Approach for the Diagnosis of Turbine Cycles in Nuclear Power Plants," Nuclear Engineering and Design, Vol. 235, Issue 14, pp. 1457-1467, June 2005. https://doi.org/10.1016/j.nucengdes.2004.12.009
  18. C. Jee, S. Jang, I. Lee, G. Heo, "Thermal Efficiency Degradation Diagnosis Method Using Regression Model," ICI 2011(ISOFIC, CSEPC, ISSNP 2011), Daejeon, Korea, August 21-25, 2011.
  19. H. Kim, Y.K. Kang, S.Y. Song, G. Heo, "Diagnosis of Feedwater Heater Performance Degradation using Fuzzy Approach", Korean Nuclear Society, 2014
  20. K. C. Cotton, "Evaluating and Improving Steam Turbine Performance," 2nd ed., Cotton Fact Inc., 1998, pp.332.
  21. J. Ma, J. Jiang, "Applications of fault detection and diagnosis methods in nuclear power plants: A review" Progress in Nuclear Energy, Vol. 53, pp. 255-266, 2011 https://doi.org/10.1016/j.pnucene.2010.12.001
  22. S. Kim and G. Heo, "Strategy of Risk-Informed Inspection for Secondary Systems in NPPs", Korean Nuclear Society, Vol. 1, pp. 991, 2012
  23. H. Eliasi, H. Davilu, M.B. Menhaj, "Adaptive fuzzy model based predictive control of nuclear steam gnerators", Nuclear Engineering and Design, Vol. 237, pp. 668-676, 2007. https://doi.org/10.1016/j.nucengdes.2006.08.007
  24. K. Salahshoor, M. Kordestani, M.S. Khoshro, "Fault detection of an industrial steam turbine using fusion of SVM(support vector machine) and ANFIS(adaptive neuro-fuzzy inference system) classifiers", Energy, Vol. 35, pp. 5472-5482, 2010. https://doi.org/10.1016/j.energy.2010.06.001
  25. W. Yang, K.Y. Lee, S.T. Junker, "Fuzzy Fault Diagnosis and Accommodation System for Hybrid Fuel-Cell/Gas-Turbine Power Plant", IEEE transactions on energy conversion, Vol. 25, No. 4, 2010.
  26. L. Su and Z. Zhao, "Performance Diagnosis of Power Plant Boiler Based on Fuzzy Comprehensive Evaluation", Electrical and Control Engineering, pp. 3076-3079, 2011.
  27. M. Daneshvar and B.F. Rad, "Data Driven Approach for Fault Detection and Diagnosis of Boiler System in Coal Fired Power Plant using Principal Component Analysis", International Review of Automatic Control, Vol. 3, No. 2, pp. 198-208, 2010
  28. A. Ajami and M. Daneshvar, "Data driven approach for fault detection and diagnosis of turbine in thermal power plant using Independent Component Analysis (ICA)", Electrical Power and Energy Systems, Vol. 43, pp. 728-735, 2012. https://doi.org/10.1016/j.ijepes.2012.06.022
  29. R.B. Joly, S.O.T. Ogaji, R. Singh, S.D. Probert, "Gas-turbine diagnostics using artificial neural-networks for a high bypass ratio military turbofan engine", Applied Energy, Vol. 78, pp. 397-418, 2004. https://doi.org/10.1016/j.apenergy.2003.10.002
  30. M. Fast, M. Assadi, S. De, "Development and multi-utility of an ANN model for an industrial gas turbine", Applied Energy, Vol. 86, pp. 9-17, 2009. https://doi.org/10.1016/j.apenergy.2008.03.018
  31. P. Deshpande, N. Warke, P. Khandare, V. Deshpande, "Thermal Power Plant Analysis Using Artificial Neural Network", NUiCONE, 2012.
  32. S. Simani and C. Fantuzzi, "Fault Diagnosis in Power Plant using Neural Networks", Information Sciences, Vol. 127, pp. 125-136, 2000. https://doi.org/10.1016/S0020-0255(00)00034-7
  33. K. Salahshoor, M. Kordestani, M. S. Khoshoro, "Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers", Energy, Vol. 35, pp. 5472-5482, 2010 https://doi.org/10.1016/j.energy.2010.06.001
  34. K.Y. Chen, L.S. Chen, M.C. Chen, C.L. Lee, "Using SVM based method for equipment fault detection in a thermal power plant", Computer in Industry, Vol. 62, pp. 42-50, 2011 https://doi.org/10.1016/j.compind.2010.05.013
  35. J. Hines and A. Usynin, "Current Computational Trends in Equipment Prognostics," International Journal of Computational Intelligence Systems, Vol. 1, No. 1, pp. 94-102, Jan. 2008. https://doi.org/10.1080/18756891.2008.9727607
  36. J.B. Coble, P. Ramuhalli, L.J. Bond, J.W. Hines, B.R. Upadhyaya, " Prognostics and Health Management in Nuclear Power Plants: A Review of Technologies and Applications", U.S. Department of Energy, PNNL-21515, July 2012.
  37. G. Heo, "Condition Monitoring using Empirical Models: Technical Review and Prospects for Nuclear Applications," Nuclear Engineering and Technology, Vol. 40, No. 1, pp. 49-68, Feb. 2008. https://doi.org/10.5516/NET.2008.40.1.049
  38. S. An, G. Heo, and S. Chang, "Detection of Process Anomalies using an Improved Statistics Learning Framework," Expert Systems with Applications, Vol. 38, Issue 3, pp. 1356-1363, 2011. https://doi.org/10.1016/j.eswa.2010.07.031
  39. R. Ahmed, G. Heo, and S. An, "Intelligent Condition-Based Maintenance Using Plant Health Index,", 8th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies, San Diego, California, USA, July 22-26, 2012.
  40. R. Mah, G. Stanley, and D. Downing, "Reconciliation and Rectification of Process Flow and Inventory Data," Ind. & EC Proc. Des. Dev., Vol. 15, Issue 1, pp. 175-183, Jan. 1976. https://doi.org/10.1021/i260057a030
  41. S. Park and G. Heo, "Simulation Based Data Reconciliation for Monitoring Power Plant Efficiency," Transactions of the Korean Nuclear Society, Jeju, Korea, Oct 21-22, 2010.
  42. A.M.C. Chan and A.K. Ahluwalia, "Feedwater Flow Measurement in U.S. Nuclear Power Generation Stations", EPRI TR-101388, Electric Power Research Institute, Nov. 1992.
  43. A. Wald and J. Wolfowitz, "Optimum Character of the Sequential Probability Ratio Test", The Annals of Mathematical Statistics, Vol. 19, No. 3, pp. 326-339, 1948 https://doi.org/10.1214/aoms/1177730197
  44. M. Camargo, W. Filho, A. Dullius, J. Maciel, M. Pinto, "Statistical Quality Control: A Case Study Research," in Proc. of IEEE International Conference on Management of Innovation and Technology, Bangkok, Thailand, Sept. 21-24, 2008, pp.746-750.
  45. GE Intelligent Platforms, "Smart Signal", Retrieved on Sep 10, 2014, http://www.ge-ip.com/products/proficysmartsignal/p3704
  46. Instep, "PRISM", Retrieved on Sep 10, 2014, http://www.instepsoftware.com/instep-software-products/prism-predictive-asset-monitoring
  47. BNF, "Plant Health Index", Retrieved on Sep 10, 2014, http://www.bnftech.com/eng/product/product03.php
  48. D.D. Benedetto, R. Berry, J. Gargus, G. Millinger, "Methods and systems for operating an automated system using a process definition model", U.S. Patent, 7,822,492, Oct 19, 2007
  49. D.H. Miller, F. Mercer, J. Popelas, "Service and diagnostic logic scan apparatus and method", U.S. Patent, 7,836,347, Oct 17, 2007
  50. Y.H. Xu, M.A. Lobuono, R.A. Rucinski, J. Simons, "Methods and systems for operating a sequence of event recorder", U.S. Patent, 8,489,554, July 16, 2013
  51. H.J. Seo, "Method Measuring of Healthy Indicia of Plant Reflected Status of Sub-component and Storage Media Thereof", Korea Patent, 1014337240000, Aug 19, 2014
  52. S.Y. Kim "Data Collecting Method for Detection and On-time warning System of Industrial Process", Korea Patent, 1010967930000, Dec 14, 2011
  53. M.A. Jianping, J. Jin, "Semi-Supervised Classification for Fault Diagnosis in Nuclear Power Plants", International Symposium on Future I&C for Nuclear Power Plants/International Symposium on Symbiotic Nuclear Power Systems 2014 (ISOFIC/ISSNP 2014), pp.24-25, Aug 24-28, 2014

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