Extended fuzzy function model with stable learning methods for online system identification

2010 ◽  
Vol 25 (2) ◽  
pp. 168-182 ◽  
Author(s):  
Selami Beyhan ◽  
Musa Alci
Keyword(s):  
System Identification ◽  
Function Model ◽  
Learning Methods ◽  
Online System ◽  
Fuzzy Function ◽  
Online System Identification ◽  
Stable Learning

scholarly journals Development of Real-Time System Identification to Detect Abnormal Operations in a Gas Turbine Cycle

2020 ◽  
Vol 142 (7) ◽  
Author(s):  
Harry Bonilla-Alvarado ◽  
Kenneth M. Bryden ◽  
Lawrence Shadle ◽  
David Tucker ◽  
Paolo Pezzini
Keyword(s):  
System Identification ◽  
Transfer Function ◽  
Power Systems ◽  
Gas Turbine ◽  
Transfer Function Model ◽  
Control Logic ◽  
Function Model ◽  
Online System ◽  
Online System Identification ◽  
Turbine Speed

Abstract This paper presents a novel online system identification methodology for monitoring the performance of power systems. This methodology was demonstrated in a gas turbine recuperated power plant designed for a hybrid configuration. A 120-kW Garrett microturbine modified to test dynamic control strategies for hybrid power systems designed at the National Energy Technology Laboratory (NETL) was used to implement and validate this online system identification methodology. The main component of this methodology consists of an empirical transfer function model implemented in parallel to the turbine speed operation and the fuel control valve, which can monitor the process response of the gas turbine system while it is operating. During fully closed-loop operations or automated control, the output of the controller, fuel valve position, and the turbine speed measurements were fed for a given period of time to a recursive algorithm that determined the transfer function parameters during the nominal condition. After the new parameters were calculated, they were fed into the transfer function model for online prediction. The turbine speed measurement was compared against the transfer function prediction, and a control logic was implemented to capture when the system operated at nominal or abnormal conditions. To validate the ability to detect abnormal conditions during dynamic operations, drifting in the performance of the gas turbine system was evaluated. A leak in the turbomachinery working fluid was emulated by bleeding 10% of the airflow from the compressor discharge to the atmosphere, and electrical load steps were performed before and after the leak. This tool could detect the leak 7 s after it had occurred, which accounted for a fuel flow increase of approximately 15.8% to maintain the same load and constant turbine speed operations.

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