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Journal ArticleDOI

Synchronous generator model identification for control application using volterra series

TLDR
Application of volterra series for identification of a synchronous generator is investigated and simulation and experimental results show good accuracy of the identified models.
Abstract
Application of volterra series for identification of a synchronous generator is investigated in this paper. For linear systems, convolution integral can be used to model the input-output data set. For nonlinear systems, however, volterra series serves as a generalization of the convolution integral. To parameterize the volterra kernels, orthogonal functions have been used. The proposed method is first applied on a seventh order nonlinear model of a synchronous generator with saturation effect and then it is tested on a micro-machine system. In this study, the field voltage is considered as the input and the active output power and the terminal voltage are considered as the outputs of the synchronous generator. Simulation and experimental results show good accuracy of the identified models.

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Citations
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Journal ArticleDOI

Nonlinear state space model identification of synchronous generators

TL;DR: In this paper, a method for identification of a synchronous generator is proposed, which uses the theoretical relations of machine parameters and the Prony method to find the state space model of the system.
Journal ArticleDOI

State-Space Model Parameter Identification in Large-Scale Power Systems

TL;DR: In this paper, a hierarchical method for model parameter identification of large-scale power systems is proposed, which uses the theoretical relations between machine parameters and the other network elements to find the state-space model of the system.
Journal ArticleDOI

Output frequency properties of nonlinear systems

TL;DR: In this article, the authors studied the periodicity of the output super-harmonic and inter-modulation frequencies of nonlinear systems and showed the mechanism of the interaction between different output harmonics incurred by different input nonlinearities in system output spectrum.
Journal ArticleDOI

Nonlinear Systems Identification and Control Via Dynamic Multitime Scales Neural Networks

TL;DR: Two NN identifiers are proposed for nonlinear systems identification via dynamic NNs with different timescales including both fast and slow phenomenon, and the controller based on the second identifier has better performance than that of the first identifier.
Proceedings ArticleDOI

Review of synchronous generator parameters estimation and model identification

TL;DR: In this article the review of synchronous generator parameter estimation and model identification presented is presented.
References
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Book

System Identification: Theory for the User

Lennart Ljung
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Book

Power System Stability and Control

P. Kundur
TL;DR: In this article, the authors present a model for the power system stability problem in modern power systems based on Synchronous Machine Theory and Modelling, and a model representation of the synchronous machine representation in stability studies.
Book

Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models

Oliver Nelles
TL;DR: This chapter discusses Optimization Techniques, which focuses on the development of Static Models, and Applications, which focus on the application of Dynamic Models.
Journal ArticleDOI

Non-linear system identification using neural networks

TL;DR: This paper investigates the identification of discrete-time nonlinear systems using neural networks with a single hidden layer using new parameter estimation algorithms derived for the neural network model based on a prediction error formulation.
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