Modeling Micro-turbines in the Distribution System Using NARX Structures

The computational burden required for the simulation is highly dependent on the complexity and accuracy of each generator model. The factory need often to provide the user a reliable low-complexity prototype model of the micro-turbine generator set, sufficiently accurate for the above application. In this paper the nonlinear autoregressive exogenous (NARX) approach is used to analyze the dynamics of this micro-turbine.

[1]  Keith R. Godfrey,et al.  Identification of processes having direction-dependent responses, with gas-turbine engine applications , 1974 .

[2]  S. A. Billings,et al.  The identification of linear and non-linear models of a turbocharged automotive diesel engine , 1989 .

[3]  David Rees,et al.  Frequency-domain identification of gas turbine dynamics , 1998, IEEE Trans. Control. Syst. Technol..

[4]  Francisco Jurado,et al.  Modeling micro-turbines on the distribution system using identification algorithms , 2003, EFTA 2003. 2003 IEEE Conference on Emerging Technologies and Factory Automation. Proceedings (Cat. No.03TH8696).

[5]  I. J. Leontaritis,et al.  Input-output parametric models for non-linear systems Part II: stochastic non-linear systems , 1985 .

[6]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[7]  W. H. Kersting.  Radial distribution test feeders , 1991 .

[8]  J. Norton.  Optimal smoothing in the identification of linear time-varying systems , 1975 .

[9]  Sheng Chen,et al.  Identification of MIMO non-linear systems using a forward-regression orthogonal estimator , 1989 .

[10]  Peter J. Fleming,et al.  Application of system identification techniques to aircraft gas turbine engines , 2001 .

[11]  Chee-Mun. Ong,et al.  Dynamic simulation of electric machinery : using MATLAB/SIMULINK , 1997 .

[12]  W. I. Rowen,et al.  Simplified Mathematical Representations of Heavy-Duty Gas Turbines , 1983 .

[13]  Tadeusz P. Dobrowiecki,et al.  Design of broadband excitation signals with a user imposed power spectrum and amplitude distribution , 1998, IMTC/98 Conference Proceedings. IEEE Instrumentation and Measurement Technology Conference. Where Instrumentation is Going (Cat. No.98CH36222).

[14]  Giorgio Rizzoni,et al.  Identification of a nonlinear MIMO IC engine model during I/M240 driving cycle for on-board diagnosis , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[15]  Peter J. Fleming,et al.  Multiobjective genetic programming for gas turbine engine model identification , 1998 .

[16]  Stephen A. Billings,et al.  Nonlinear model validation using correlation tests , 1994 .

[17]  David Rees,et al.  Testing and Modelling Gas Turbines Using Multisine Signals and Frequency-Domain Techniques , 1998 .

[18]  David Rees,et al.  LINEAR AND NONLINEAR GAS TURBINE COMPUTER MODELLING , 2000 .

[19]  Kumpati S. Narendra,et al.  Identification and control , 1998 .

[20]  David Rees,et al.  Testing and Modeling Gas Turbines Using Multisine Signals and Frequency-Domain Techniques , 1999 .

[21]  S. A. Billings,et al.  Spectral analysis for non-linear systems, Part I: Parametric non-linear spectral analysis , 1989 .

[22]  Francisco Jurado,et al.  Neuro-fuzzy controller for gas turbine in biomass-based electric power plant , 2002 .

[23]  David Rees,et al.  Identifying linear models of systems suffering nonlinear distortions, with a gas turbine application , 1995 .

[24]  E.M.A.M. Mendes.  Important issues of system identification using algorithms for structure selection: do noise terms have any influence when the data are chaotic? , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).