TY - GEN
T1 - Neural network based system for detecting and diagnosing faults in steam turbine of thermal power plant
AU - Dhini, Arian
AU - Putro, Benyamin Kusumo
AU - Prajitno, Isti Surjandari
PY - 2018/1/12
Y1 - 2018/1/12
N2 - 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.
AB - 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.
KW - data driven approach
KW - fault detection and diagnosis
KW - neural network
KW - power plant
KW - steam turbine
UR - http://www.scopus.com/inward/record.url?scp=85050609843&partnerID=8YFLogxK
U2 - 10.1109/ICAwST.2017.8256435
DO - 10.1109/ICAwST.2017.8256435
M3 - Conference contribution
T3 - Proceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017
SP - 149
EP - 154
BT - Proceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Awareness Science and Technology, iCAST 2017
Y2 - 8 November 2017 through 10 November 2017
ER -