scholarly journals Development of wood as an alternative fuel for large power-generating systems. Part III. Computer simulation of the dynamic response of an industrial gas turbine with large-volume combustion and filtering system. Final report

1981 ◽  
Author(s):  
N. Hamrick
Keyword(s):  
Computer Simulation ◽  
Dynamic Response ◽  
Gas Turbine ◽  
Large Volume ◽  
Alternative Fuel ◽  
Final Report ◽  
Large Power ◽  
Industrial Gas Turbine

Industrial gas turbine combustion performance test of DME to use as an alternative fuel for power generation

Fuel ◽  
2009 ◽  
Vol 88 (4) ◽  
pp. 657-662 ◽  
Author(s):  
Min Chul Lee ◽  
Seok Bin Seo ◽  
Jae Hwa Chung ◽  
Yong Jin Joo ◽  
Dal Hong Ahn
Keyword(s):  
Power Generation ◽  
Gas Turbine ◽  
Performance Test ◽  
Alternative Fuel ◽  
Combustion Performance ◽  
Gas Turbine Combustion ◽  
Industrial Gas Turbine

Development of L1-norm sliding mode observer for sensor fault diagnosis of an industrial gas turbine

2021 ◽  
pp. 095965182199617
Author(s):  
Mahyar Akbari ◽  
Abdol Majid Khoshnood ◽  
Saied Irani
Keyword(s):  
Fault Detection ◽  
State Space ◽  
Gas Turbine ◽  
Sliding Mode ◽  
State Space Model ◽  
Sensor Fault ◽  
Space Model ◽  
Decision Logic ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L1-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L1-norm, reducing the estimation noise while increasing the accuracy. The L1-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L1-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.

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