The determination of third order linear models from a seventh order nonlinear jet engine model

LaLonde,; Hartley,; Abreu-Garcia, De

doi:10.1109/icsyse.1989.48716

Cited by 4 publications

(1 citation statement)

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“…A brief description of the system identification procedure is given here-for details consult Kaufman and Ravi (1994). Also see Eldem and Duyar (1993), Lalonde, et al (1989), Merrill (1984, and Pimentel (1983) for past work in identification and modeling applied to gas turbine systems.…”

Section: System Modelingmentioning

confidence: 99%

Multivariable Control of Industrial Gas Turbines: Field Test Results

Rajamani¹,

Norman²

1996

Volume 5: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation;

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Rejecting, or reducing, the effect of external disturbances on process parameters is an important problem in control design. In this paper we apply multivariable control techniques to reduce the effect of input disturbances, such as variations in the line frequency, on key internal parameters of an industrial gas turbine. The parameter we are most interested in is the combustion reference temperature, an estimated variable that is used by the controller to schedule division of fuel to various fuel nozzles and determine switching points between combustion modes. The dynamic response of this parameter correlates well with the dynamic response of fuel air ratio inside the combustor. Therefore, an important step in improving combustor performance is better regulation of the combustion reference temperature. We show that the use of a multivariable controller in place of the existing decentralized controller makes the disturbance rejection problem much easier to solve. As the gas turbine is inherently a multivariable system — i.e. the inputs, fuel and air, are coupled to the outputs, power and exhaust temperature — this result is not entirely surprising. We use a frequency domain, control design technique known as the Edmund’s method. The linear models are obtained using system identification techniques. We present results from a field test of the controller (implemented on a GE Frame 7E turbine) in the form of data comparing the response of the multivariable controller with that of the existing (decentralized) controller. These results clearly show that by using a multivariable controller the effects of the external disturbances can be reduced by a factor of 3 when compared with the existing design.

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Section: System Modelingmentioning

confidence: 99%