Fault Detection and Isolation of Load Mutation Caused by Electrical Interference of Single-Shaft Combined Cycle Power Plant
Source abstract
Abstract
Because the generator power-measuring equipment is often accompanied by electrical interference in a complex electromagnetic environment in an actual thermal power plant, the output signal will change or even distort while it passes through the devices of acquisition and conversion. Several practical cases have found that the abnormal generation change phenomenon, impulse or oscillation caused by electrical interference, has different effects on the load regulation of steam turbines. These faults also exist in combined-cycle power plants (CCPPs). However, the insufficient installed capacity and operating life of CCPPs domestically cause similar load mutation failures that are scarcely found. We had to acknowledge that CCPPs and steam turbine regulation characteristics differ. It is of great value to study the influence of differences in load mutation on the load regulation of single-shaft CCPPs. We extracted the fault characteristics of two sudden load change phenomena using the operation data of an actual steam turbine and analyzed them through simulation. Furthermore, a fault detection and isolation method for sudden load changes in a single-shaft CCPP was proposed and the simulation results verified the method’s effectiveness.
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Research context
OpenAlex topics: Fault Detection and Control Systems · Hydraulic and Pneumatic Systems · Machine Fault Diagnosis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, China
College of Power and Energy Engineering, Harbin Engineering University, Harbin 150009, China
School of Energy Science & Engineering, Harbin Institute of Technology, Harbin 150001, China
Funding
China Postdoctoral Science Foundation · National Science and Technology Major Project
References (23)
Show source-provided references
1. Thermodynamic evaluation of solar integration into a natural gas combined cycle power plant10.1016/j.renene.2014.08.073 ↗
2. Evaluation of novel configurations of natural gas combined cycle (NGCC) power plants for load-following operation using dynamic modeling and optimization10.1021/acs.energyfuels.9b03036 ↗
3. Challenges of renewable energy penetration on power system flexibility: A survey10.1016/j.esr.2020.100539 ↗
4. Energy, exergy and parametric analysis of a combined cycle power plant10.1016/j.tsep.2019.100450 ↗
5. Performance comparison between steam injected gas turbine and combined cycle during frequency drops10.3390/en8087582 ↗
6. Smith, R.W. (2022). Steam turbine cycles and cycle design optimization: Combined cycle power plants. Advances in Steam Turbines for Modern Power Plants, Woodhead Publishing.10.1016/B978-0-12-824359-6.00001-9 ↗
7. An efficient algorithm for atomic decomposition of power quality disturbance signals using convolutional neural network10.1016/j.epsr.2022.107790 ↗
8. Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods10.1016/j.ijepes.2014.02.027 ↗
9. Ghosh, T., Martinsen, K., and Dan, P.K. (2019, January 8). Data-driven beetle antennae search algorithm for electrical power modeling of a combined cycle power plant. Proceedings of the World Congress on Global Optimization, Metz, France.10.1007/978-3-030-21803-4_90 ↗
10. Lorencin, I., Anđelić, N., Mrzljak, V., and Car, Z. (2019). Genetic algorithm approach to design of multi-layer perceptron for combined cycle power plant electrical power output estimation. Energies, 12.10.3390/en12224352 ↗
11. Power Prediction of Combined Cycle Power Plant (CCPP) Using Machine Learning Algorithm-Based Paradigm10.1155/2021/9966395 ↗
12. Therkorn, D. (2005, January 6). Remote monitoring and diagnostic for combined-cycle power plants. Proceedings of the Turbo Expo: Power for Land, Sea, and Air, Reno, NV, USA.10.1115/GT2005-68710 ↗
13. Davallo, H.E., Bahrevar, R., and Chaibakhsh, A. (2019, January 20). Fault diagnosis of Combined Cycle Power Plant Using ELM. Proceedings of the 2019 7th International Conference on Robotics and Mechatronics (ICRoM), Tehran, Iran.10.1109/ICRoM48714.2019.9071851 ↗
14. Fault tolerant measurement system based on Takagi–Sugeno fuzzy models for a gas turbine in a combined cycle power plant10.1016/j.fss.2011.02.011 ↗
15. Camporeale, S., Dambrosio, L., Milella, A., Mastrovito, M., and Fortunato, B. (2003, January 15–17). Fault diagnosis of combined cycle gas turbine components using feed forward neural networks. Proceedings of the Turbo Expo: Power for Land, Sea, and Air, Atlanta, GA, USA.10.1115/GT2003-38742 ↗
16. Monitoring strategies for a combined cycle electric power generator10.1016/j.apenergy.2010.02.017 ↗
17. Al-Tameemi, A.A., Wang, K., Li, L., and Zalhaf, A.S. (2021, January 15). A Convolutional Attention Mechanism-based Capsule Network scheme for Gearbox fault diagnosis using Two directions signals and Noise Environment. Proceedings of the 2021 IEEE 5th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Xi’an, China.10.1109/ITNEC52019.2021.9586957 ↗
18. Fault Mechanism and Diagnosis Method of Typical Load Mutation Problem of Turbo-generator Set
19. Qasem, M.A., and Albagul, A. (2021, January 25). Performance investigation of the gas turbine with the combined power plant. Proceedings of the 2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, Tripoli, Libya.10.1109/MI-STA52233.2021.9464454 ↗
20. A strong tracking filter based multiple model approach for gas turbine fault diagnosis10.1007/s12206-017-1248-0 ↗
21. Multiple model-based detection and estimation scheme for gas turbine sensor and gas path fault simultaneous diagnosis10.1007/s12206-019-0346-6 ↗
22. Thermodynamic performance simulation and concise formulas for triple-pressure reheat HRSG of gas–steam combined cycle under off-design condition10.1016/j.enconman.2016.05.088 ↗
23. Vector-based deterioration index for gas turbine gas-path prognostics modeling framework10.1016/j.energy.2020.119198 ↗