Enhancing Reliability of Fuel Gas System at Combined Cycle Power Plant
DOI:
https://doi.org/10.25729/esr.2025.04.0011Keywords:
combined cycle power plant, diagnostic alarm, fault detection, associated petroleum gas suppl, safety interlock, gas chromatography, reliabilityAbstract
This paper presents a methodology to boost the reliability of a combined natural gas and associated petroleum gas system (fuel gas system, FGS) for a gas turbine unit in a combined cycle power plant. The use of failure mode, effects, and diagnostic analysis (FMEDA) is proposed to avoid unplanned shutdowns of the power plant. This method identifies and evaluates potential types of failures, develops measures to reduce them, and establishes a new protection system. The system includes a gas analysis system (GAS), a shut-off valve system (SVS), a fuel gas controller (FGC), and workstations for an engineer and operator. The gas analysis system has two automatic subsystems with different measurement methods. One of them includes three gas chromatograph analyzers that operate according to the 2-out-of-3 voting. The results of gas chromatography and the diagnostic archive of alarms serve as the basis for analyzing the causes of possible failures. Reliability models were developed to confirm the effectiveness of using diagnostic data from gas analyzers within the gas subsystem. They employ a gas chromatography and a common fuel gas controller. The FMEDA findings demonstrate that a new safety interlock can be implemented without any additional financial outlay for software and hardware.
References
P. A. Batrakov, E. A. Ryzhnikova, A. A. Batrakova, “The use of associated petroleum gas by burning it in a mixture with natural gas in a combined cycle gas plant to generate mechanical energy and electricity,” in 2024 Dynamics of Systems, Mechanisms and Machines (Dynamics 2024), vol. 1, Omsk, Russian Federation, Nov. 12–14, 2024, pp. 1–8, 2024.
S. Khalid, et al, “Advances in fault detection and diagnosis for thermal power plants: A review of intelligent techniques,” Mathematics, vol. 11, no. 8, Art. no. 1767, 2023.
C. Pengchao, “Advancements and future outlook of safety monitoring, inspection and assessment technologies for oil and gas pipeline networks,” Journal of Pipeline Science and Engineering, vol. 5, pp. 100–267, 2025.
R. P. Bam, R. S. P. Gaonkar, C. P. George, “Machine learning-based predictive maintenance for diagnostics and prognostics of engineering systems,” in Intelligent Prognostics for Engineering Systems with Machine Learning Techniques. Boca Raton, USA: CRC Press, 2023, pp. 211–243.
S. Liu, et al, “ECoalVis: Visual analysis of control strategies in coal-fired power plants,” IEEE transactions on visualization and computer graphics, vol. 29, no. 1, pp. 1091–1101, 2022.
D. I. Candra, C. A. W. Tamayo, “Optimization for biogas power plants using automatic control of gas pressures,” Journal of Mechatronics, Electrical Power, and Vehicular Technology, vol. 4, no. 1, pp. 9–16, 2013.
M. H. Sahraei, et al, “A survey on current advanced IGCC power plant technologies, sensors and control systems,” Fuel, vol. 137, pp. 245–259, 2014.
A. A. Kapansky, “Modern strategies for using artificial intelligence to prevent accidents in technical resource supply systems,” Bulletin of Kazan State Power Engineering University, vol. 16, no. 1 (61), pp. 38–51, 2024. (In Russian)
S. N. Kharlap, et al, “Features of methods for analyzing the types and consequences of failures of harvester devices,” in Proc. Problems of transport safety materials of the XII International Scientific and Practical Conference dedicated to the 160th anniversary of the Belarusian Railway, Gomel, Belarus, 2022, pp. 227–230. (In Russian)
“Analysis of types and consequences (criticality, diagnosability) of failures (FMEA/FMECA/FMEDA).” [Online]. Available: http://www.kconsult-cis.com/fmea-fmeca.html. Accessed on: Oct. 15, 2025. (In Russian)
L. Wang, F. Zhang, “Transmitter fault diagnosis technology for high-risk complex scenario,” in Proc. 2024 6th International Conference on System Reliability and Safety Engineering (SRSE), Hangzhou, China, 2024, pp. 378–381.
O. Ivasiuk, V. Kharchenko, “Using the FMEDA/FIT verification method to assess the cybersecurity of a programable logic controller: a new interpretation of the SIS principle,” Aerospace Technic and Technology, no. 1, pp. 76–90, 2024.
M. Catelani, L. Ciani, G. Patrizi, “Logic solver diagnostics in safety instrumented systems for oil and gas applications,” Safety, vol. 8, no. 1, Art. no. 15, 2022.
M. Catelani, L. Ciani, V. Luongo, “The FMEDA approach to improve the safety assessment according to the IEC61508,” Microelectronics Reliability, vol. 50, no. 9–11, pp. 1230–1235, 2010.
B. Hitchcock, “Failure Modes, Effects and Diagnostic Analysis,” Series 12 Switch. United Electric Controls, Watertown, MA, USA. Contract Number: Q20/06-041. Rep. No.: UEC 20/06-041 R001. Version V1, Revision R1, Nov. 20, 2020.
M. Chaari, et al, “A model-based and simulation-assisted FMEDA approach for safety-relevant E/E systems,” in Proc. the 52nd Annual Design Automation Conference, San Francisco, USA, 2015, pp. 1–6.
D. K. Kostrin, “Modern electronic gas sensors: an overview of the basic physical principles of operation and promising developments,” Physics, vol. 17, no. 4, pp. 5–20, 2024. (In Russian)
A. I. Kostogryzov, “On models and methods of probabilistic analysis of information security in standardized processes of system engineering,” Cybersecurity Issues, no. 6 (52), pp. 71–82, 2022. (In Russian)
A. Julsereewong, T. Thepmanee, “Design and implementation of functional safety for repairable systems,” in Proc. the SICE annual conference, Nara, Japan, 2018. pp. 1638–1643.
A. P. Durakovsky, S. V. Ponomarev, “A model of a system for countering software tools for studying code,” Information Technology Security, vol. 22, no. 1, pp. 22–24, 2015. (In Russian)
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