A dynamic prognosis scheme for flexible operation of gas turbines

TSOUTSANIS, Elias, MESKIN, Nader, BENAMMAR, Mohieddine and KHORASANI, Khashayar (2016). A dynamic prognosis scheme for flexible operation of gas turbines. Applied Energy, 164, 686-701.

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Official URL: http://www.sciencedirect.com/science/article/pii/S...
Link to published version:: https://doi.org/10.1016/j.apenergy.2015.11.104
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The increase in energy demand has led to expansion of renewable energy sources and their integration into a more diverse energy mix. Consequently the operation of thermal power plants, which are spearheaded by the gas turbine technology, has been affected. Gas turbines are now required to operate more flexible in grid supporting modes that include part-load and transient operations. Therefore, condition based maintenance should encapsulate this recent shift in the gas turbine's role by taking into account dynamic operating conditions for diagnostic and prognostic purposes. In this paper, a novel scheme for performance-based prognostics of industrial gas turbines operating under dynamic conditions is proposed and developed. The concept of performance adaptation is introduced and implemented through a dynamic engine model that is developed in Matlab/Simulink environment for diagnosing and prognosing the health of gas turbine components. Our proposed scheme is tested under variable ambient conditions corresponding to dynamic operational modes of the gas turbine for estimating and predicting multiple component degradations. The diagnosis task developed is based on an adaptive method and is performed in a sliding window-based manner. A regression-based method is then implemented to locally represent the diagnostic information for subsequently forecasting the performance behavior of the engine. The accuracy of the proposed prognosis scheme is evaluated through the Probability Density Function (PDF) and the Remaining Useful Life (RUL) metrics. The results demonstrate a promising prospect of our proposed methodology for detecting and predicting accurately and efficiently the performance of gas turbine components as they degrade over time. © 2015 Elsevier Ltd.

Item Type: Article
Additional Information: cited By 5
Uncontrolled Keywords: Engines; Forecasting; Gas plants; Gas turbine power plants; Gas turbines; Gases; Plasma diagnostics; Probability density function; Renewable energy resources; Systems engineering; Thermoelectric power plants, Adaptive methods; Condition based maintenance; Gas Turbine Technologies; MATLAB/Simulink environment; Operational flexibility; Probability density function (pdf); Prognostics; Renewable energy source, Turbine components, energy resource; methodology; operations technology; performance assessment; power plant; renewable resource; thermal power; turbine
Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Engineering and Mathematics
Identification Number: https://doi.org/10.1016/j.apenergy.2015.11.104
Page Range: 686-701
Depositing User: Elias Tsoutsanis
Date Deposited: 16 Aug 2017 15:58
Last Modified: 18 Mar 2021 04:10
URI: https://shura.shu.ac.uk/id/eprint/16178

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