Anomaly Detection in the Pressure Turbine of Thermal Power Plant: A Case Study
Abstract
This study examines the interpretation of anomalous vibration patterns detected in vibration data collected from turbine bearings in a thermal power plant, along with the subsequent fault identification and interventions. The uninterrupted operation of critical equipment is of paramount importance for continuous energy production, making effective condition monitoring methods like vibration analysis essential. The research analyzed data from vibration sensors mounted on the turbine bearings. The findings revealed friction and uneven load distribution in the 2nd and 3rd bearings. Based on these results, necessary repairs and adjustments were made, including checking the low-pressure turbine casing and bearing clearances, and realigning the rotor. This study demonstrates that vibration analysis is a highly sensitive and powerful technique for the advanced detection of malfunctions in critical equipment. Implementing condition monitoring in power plants and similar facilities helps reduce downtime and increase efficiency and reliability, thereby contributing to profitability and energy supply security.
Keywords
Thermal power plant, Pressure turbine, Condition monitoring, Vibration analysis
References
- Agrawal, V., Panigrahi, B. K., & Subbarao, P. M. V. (2015). Review of control and fault diagnosis methods applied to coal mills. Journal of Process Control. https://doi.org/10.1016/j.jprocont.2015.04.006
- Akademia Baru, P., Khattak, M. A., Mohd Ali, N. S., Zainal Abidin, N. H., Azhar, N. S., & Omar, M. H. (2016). Common type of turbines in power plant: A review. Journal of Advanced Research in Applied Sciences and Engineering Technology.
- Alirahmi, S. M., Assareh, E., Arabkoohsar, A., Yu, H., Hosseini, S. M., & Wang, X. (2022). Development and multi-criteria optimization of a solar thermal power plant integrated with PEM electrolyzer and thermoelectric generator. International Journal of Hydrogen Energy, 47(57), 23919–23934. https://doi.org/10.1016/j.ijhydene.2022.05.196
- Anagnostou, G., Boem, F., Kuenzel, S., Pal, B. C., & Parisini, T. (2018). Observer-based anomaly detection of synchronous generators for power systems monitoring. IEEE Transactions on Power Systems. https://doi.org/10.1109/TPWRS.2017.2771278
- Barszcz, T., & Czop, P. (2011). A feedwater heater model intended for model-based diagnostics of power plant installations. Applied Thermal Engineering, 31(8–9), 1357–1367. https://doi.org/10.1016/j.applthermaleng.2010.12.012
- Chaibakhsh, A., & Ghaffari, A. (2008). Steam turbine model. Simulation Modelling Practice and Theory, 16(9), 1145–1162. https://doi.org/10.1016/j.simpat.2008.05.017
- Danguche, I., & Taifa, I. (2023). Factors influencing total productive maintenance implementation for thermal generation plants. Tanzania Journal of Engineering and Technology. https://doi.org/10.52339/tjet.v42i1.892
- Dhamande, L. S., Bhaurkar, V. P., & Patil, P. N. (2023). Vibration analysis of induced draught fan: A case study. Materials Today: Proceedings, 72, 657–663. https://doi.org/10.1016/j.matpr.2022.08.329
- Dhini, A., Kusumoputro, B., & Surjandari, I. (2017). Neural network based system for detecting and diagnosing faults in steam turbine of thermal power plant. Proceedings of the IEEE International Conference on Awareness Science and Technology. https://doi.org/10.1109/ICAwST.2017.8256435
- Doshi, S., Katoch, A., Suresh, A., Razak, F. A., Datta, S., Madhavan, S., Zanhar, C. M., & Gundabattini, E. (2021). A review on vibrations in various turbomachines such as fans, compressors, turbines and pumps. Journal of Vibration Engineering and Technologies. https://doi.org/10.1007/s42417-021-00313-x