Publication for Volume-3 Issue-9, August 2018

Conceptual Development of Non-Conventional Steam Turbine of Thermal Power Plant
Article Type
Research Article
Author Name(s)
Amar Kumar Shrivastava, Rewa institute of technology; Pushpraj Singh ,Rewa institute of technology
Research Area
Steam turbine is the main system of a steam power plant and critical for power generation. Therefore, there is urgency for maintaining the reliability and availability of a steam turbine. A fast and accurate fault detection and diagnosis (FDD) system should be developed as an integral part to prevent a system from catastrophic disaster due to unhandled failures. Many previous studies applied model-based methods to build the FDD system. However, using those approaches required prior knowledge of the system. The power plant is a complex system, where comprehensive process knowledge is a real challenge. On the other hand, power plants have implemented condition monitoring which resulted in process monitoring data. Therefore, this study proposed a data-driven FDD system in a steam turbine of thermal power plant. The study used the process monitoring data from an Indonesian government owned steam power plant. A neural network based classifier was constructed to detect and diagnose faults as well as normal operating condition based on three scenarios. The result showed that the last two scenarios, with and without PCA approach, outperformed the first scenario which only used selected process parameters. The study demonstrated the superiority of data driven approach in the fault detection and diagnosis area.
Keywords : Data Driven Approach, Fault Detection and Diagnosis, Neural Network, Power Plant, Steam Turbine
[1]	Z. Gao, C. Cecati, and S. X. Ding, "A survey of fault diagnosis and fault-tolerant techniques—Part I: Fault diagnosis with model-based and signal-based approaches," IEEE Transactions on Industrial Electronics, vol. 62, pp. 3757-3767, 2015.
[2]	V. Venkatasubramanian, "Systemic failures: challenges and opportunities in risk management in complex systems," AIChE Journal, vol. 57, pp. 2-9, 2011.
[3]	R. Isermann and P. Balle, "Trends in the application of model-based fault detection and diagnosis of technical processes," Control Engineering Practice, vol. 5, pp. 709-719, 1997.
[4]	V. Venkatasubramanian, R. Rengaswamy, K. Yin, and S. N. Kavuri, "A review of process fault detection and diagnosis: Part I: Quantitative model-based methods," Computers & Chemical Engineering, vol. 27, pp. 293-311, 2003.
[5]	Z. Gao, C. Cecati, and S. X. Ding, "A survey of fault diagnosis and fault-tolerant techniques—Part II: Fault diagnosis with knowledge-based and hybrid/active approaches," IEEE Transactions on Industrial Electronics, vol. 62, pp. 3768-3774, 2015.
[6]	E. B. Woodruff, H. B. Lammers, and T. F. Lammers, Steam-plant operation: McGraw-Hill New York, 1998.
[7]	Y. Changfeng, H. Zhang, and W. Lixiao, "A novel real-time fault diagnostic system for steam turbine generator set by using strata hierarchical artificial neural network," Energy and Power Engineering, vol. 1, p. 7, 2009.
[8]	A. Chaibakhsh and A. Ghaffari, "Steam turbine model," Simulation Modelling Practice and Theory, vol. 16, pp. 1145-1162, 2008.
[9]	M. A. Arjona López, C. Hernández Flores, and E. Gleason GarcÕғa, "An intelligent tutoring system for turbine startup training of electrical power plant operators," Expert Systems with Applications, vol. 24, pp. 95-101, 2003.
[10]	A. S. LeƱzerovich, Steam turbines for modern fossil-fuel power plants: The Fairmont Press, Inc., 2008.
[11]	O. Jonas and L. Machemer, "Steam turbine corrosion and deposits problems and solutions," in Proc. of 37th Turbomachinery Symposium, 2008, pp. 8-11.
[12]	K. Fujiyama, T. Kubo, Y. Akikuni, T. Fujiwara, H. Kodama, M. Okazaki, and T. Kawabata, "An integrated approach of risk based maintenance for steam turbine components," OMMI Internet Journal, vol. 4, pp. 1-18, 2007.
[13]	G. Guglielmi, T. Parisini, and G. Rossi, "Keynote paper: Fault diagnosis and neural networks: A power plant application," Control Engineering Practice, vol. 3, pp. 601-620, 1995.
[14]	C. Karlsson, J. Arriagada, and M. Genrup, "Detection and interactive isolation of faults in steam turbines to support maintenance decisions," Simulation Modelling Practice and Theory, vol. 16, pp. 1689-1703, 2008.
[15]	A. Kusiak and Z. Song, "Sensor fault detection in power plants," Journal of Energy Engineering, vol. 135, pp. 127-137, 2009.
[16]	A. Rasaienia, B. Moshiri, and M. Moezzi, "Feature-based fault detection of industrial gas turbines using neural networks," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 21, pp. 1340-1350, 2013.
[17]	M. Todd, S. D. McArthur, J. R. McDonald, and S. J. Shaw, "A semiautomatic approach to deriving turbine generator diagnostic knowledge," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 37, pp. 979-992, 2007.
[18]	G. Bin, J. Gao, X. Li, and B. Dhillon, "Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network," Mechanical Systems and Signal Processing, vol. 27, pp. 696-711, 2012.
[19]	J. Shlens, "A tutorial on principal component analysis," arXiv preprint arXiv: 1404.1100, 2014. is a social networking website for academics. It was launched in September 2008 and had over 21 million registered users as of April 2015.The platform can be used to share papers, monitor their impact, and follow the research in a particular field. was founded by Richard Price, who raised $600,000 from Spark Ventures, Brent Hoberman, and others.
Learn More
Google Scholar is a freely accessible web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines. Released in beta in November 2004, the Google Scholar index includes most peer-reviewed online journals of Europe and America's largest scholarly publishers,plus scholarly books and other non-peer reviewed journals.
Learn More
Issuu is a free electronic publishing platform for magazines, catalogs, newspapers and more. As a digital newsstand with over 21 million publications and 85 million active readers
Learn More
ResearchBib is a free academic database that indexes and provides open access to peer-reviewed journals, full text papers, research conferences & positions.
Learn More
Scribd is a digital library and ebook, audiobook and comic book subscription service that includes one million titles. In addition, Scribd hosts 60 million documents on its open publishing platform.
Learn More