Neural network based system for detecting and diagnosing faults in steam turbine of thermal power plant

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages149-154
Number of pages6
ISBN (Electronic)9781538629659
DOIs
Publication statusPublished - 12 Jan 2018
Event8th IEEE International Conference on Awareness Science and Technology, iCAST 2017 - Taichung, Taiwan, Province of China
Duration: 8 Nov 201710 Nov 2017

Publication series

NameProceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017
Volume2018-January

Conference

Conference8th IEEE International Conference on Awareness Science and Technology, iCAST 2017
CountryTaiwan, Province of China
CityTaichung
Period8/11/1710/11/17

Keywords

  • data driven approach
  • fault detection and diagnosis
  • neural network
  • power plant
  • steam turbine

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    Dhini, A., Putro, B. K., & Prajitno, I. S. (2018). Neural network based system for detecting and diagnosing faults in steam turbine of thermal power plant. In Proceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017 (pp. 149-154). (Proceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAwST.2017.8256435