Transfer function identification in power system applications

J.R. Smith; F. Fatehi; C.S. Woods; J.F. Hauer; D.J. Trudnowski

doi:10.1109/59.260866

Transfer function identification in power system applications

IEEE Transactions on Power Systems ◽

10.1109/59.260866 ◽

1993 ◽

Vol 8 (3) ◽

pp. 1282-1290 ◽

Cited By ~ 104

Author(s):

J.R. Smith ◽

F. Fatehi ◽

C.S. Woods ◽

J.F. Hauer ◽

D.J. Trudnowski

Keyword(s):

Transfer Function ◽

Power System ◽

Function Identification

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General formulation of transfer function identification for power system based on WAMS

2009 IEEE/PES Power Systems Conference and Exposition ◽

10.1109/psce.2009.4840090 ◽

2009 ◽

Author(s):

Jun Qi ◽

Quanyuan Jiang ◽

Yijia Cao

Keyword(s):

Transfer Function ◽

Power System ◽

Function Identification

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Fourier Series-Based Neural Networks for Vehicle System Identification

10.1115/imece1999-0331 ◽

1999 ◽

Author(s):

Imtiaz Haque ◽

Juergen Schuller

Keyword(s):

Neural Networks ◽

Fourier Series ◽

System Identification ◽

Transfer Function ◽

Parameter Identification ◽

Frequency Domain ◽

Vehicle System ◽

Non Linear ◽

Function Identification ◽

Non Linear System

Abstract The use of neural networks in system identification is an emerging field. Neural networks have become popular in recent years as a means to identify linear and non-linear systems whose characteristics are unknown. The success of sigmoidal networks in parameter identification has been limited. However, harmonic activation-based neural networks, recent arrivals in the field of neural networks, have shown excellent promise in linear and non-linear system parameter identification. They have been shown to have excellent generalization capability, computational parallelism, absence of local minima, and good convergence properties. They can be used in the time and frequency domain. This paper presents the application of a special class of such networks, namely Fourier Series neural networks (FSNN) to vehicle system identification. In this paper, the applications of the FSNNs are limited to the frequency domain. Two examples are presented. The results of the identification are based on simulation data. The first example demonstrates the transfer function identification of a two-degree-of freedom lateral dynamics model of an automobile. The second example involves transfer function identification for a quarter car model. The network set-up for such identification is described. The results of the network identification are compared with theory. The results indicate excellent prediction properties of such networks.

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