A Frequency Localizing Basis Function-Based IV Method for Wideband System Identification

Gilson, Marion; Welsh, James S.; Garnier, Hugues

doi:10.1109/tcst.2016.2646320

Cited by 16 publications

(8 citation statements)

References 27 publications

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“…Let G ( s ) be the required transfer function with nonlinear polynomials B ( s ) and A ( s ) with order n and m , where n ≥ m and A ( s ) ≠ 0.

G false(s false) = \frac{b_{m} s^{m} + b_{m - 1} s^{m - 1} + \dots + b_{1} s + b_{0}}{a_{n} s^{n} + a_{n - 1} s^{n - 1} + \dots + a_{1} s + 1}

The transfer‐function can be computed by using multiple linear curve fitting method based on the discrete values of FCIM obtained at different frequencies [13]. The matrix equation can be written as

{boldG}_{N \times 1} = {boldΛ}_{N \times false(n + m + 1 false)} θ_{false(n + m + 1 false) \times 1}

Where θ ( n+m+ 1)×1 is vector containing the coefficients of polynomials B ( s ) and A ( s ).…”

Section: Fcim and Its Calculation Methodsmentioning

confidence: 99%

Frequency‐coupled impedance model based subsynchronous oscillation analysis for direct‐drive wind turbines connected to a weak AC power system

Liu

Xie

Shair

et al. 2019

J. eng.

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“…Let G ( s ) be the required transfer function with nonlinear polynomials B ( s ) and A ( s ) with order n and m , where n ≥ m and A ( s ) ≠ 0.

G false(s false) = \frac{b_{m} s^{m} + b_{m - 1} s^{m - 1} + \dots + b_{1} s + b_{0}}{a_{n} s^{n} + a_{n - 1} s^{n - 1} + \dots + a_{1} s + 1}

{boldG}_{N \times 1} = {boldΛ}_{N \times false(n + m + 1 false)} θ_{false(n + m + 1 false) \times 1}

Where θ ( n+m+ 1)×1 is vector containing the coefficients of polynomials B ( s ) and A ( s ).…”

Section: Fcim and Its Calculation Methodsmentioning

confidence: 99%

Frequency‐coupled impedance model based subsynchronous oscillation analysis for direct‐drive wind turbines connected to a weak AC power system

Liu

Xie

Shair

et al. 2019

J. eng.

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“…The transfer function can be computed using multiple linear curve-fitting method based on the FCIMs obtained at discrete frequencies [29]. Let Ω(s) be the required transfer function, representing the transfer functions for admittances or impedances…”

Section: Computing the Transfer Function Via Curve-fitting Techniquementioning

confidence: 99%

Frequency‐coupled impedance model‐based sub‐synchronous interaction analysis for direct‐drive wind turbines connected to a weak AC grid

Liu

Xie

Shair

et al. 2019

IET Renewable Power Generation

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Recently, emerging sub-synchronous oscillation (SSO) issues caused by the interaction between power electronic converters and weak AC grids have provoked serious stability concerns. Impedance-based modelling approaches have been widely employed for stability analysis of such interactions. In this study, a sequence-domain frequency-coupled impedance model (FCIM) and the corresponding stability criterion are proposed. First, a fast identification method of the FCIM is proposed, in which the impedance-frequency curves of the FCIMs at multiple frequencies are measured, followed by identification of their transfer function models. Next, the proposed method is applied to model a practical system, where a SSO event once occurred due to the interactions between direct-drive wind turbines and a weak AC grid. The individual FCIMs of wind turbine generators, steam turbine generators and static var generator in this system are derived. After that, an aggregated FCIM is formed by combining the FCIMs without extra coordinate transformation. Then, the aggregated FCIM-based stability analysis is carried out to investigate the interaction between the wind farms and weak AC grids. In the end, the results of FCIM-based stability analysis are verified by time-domain simulations.

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“…In this topic, in additional to a higher level of resonance, real data from measurement is used to estimate a model. The experimental setup and measurement samples have been presented in (Welsh and Goodwin, 2003;Gilson et al, 2018), the frequency axis is defined from 10 rad/s to 600 rad/s and 1600 points were used to estimate the model. Fig.…”

Section: Real Data From a Resonant Beammentioning

confidence: 99%

“…Fig. 3 illustrates the experimental setup described in Gilson et al (2018). The bar consists of a 60cm long uniform aluminum beam and a pair of piezoelectric elements are attached symmetrically to either side of the beam.…”

Section: Real Data From a Resonant Beammentioning

confidence: 99%

On the use of Frequency-Domain Subspace-based System Identification for Estimating Resonant Systems

Rodrigues

Oliveira

Santo³

2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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In this paper, a parametric identification of high order resonant systems from frequency response data is analyzed. Among many techniques for obtaining black-box models for such systems, subspace-based algorithms stand out. Subspace methods uses geometric projections to obtain specific information necessary to compute the system extended observability matrix and, eventually, to obtain a state-space model representation. Throughout the paper, the method is also shown to use only well conditioned matrices to avoid numerical problems with high order models. The validity of the algorithm is firstly demonstrated on a simulated data of a synthetic 15th order system. Then, in order to state the systems effectiveness, the method is applied to actual data extracted from a resonant beam.

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A Frequency Localizing Basis Function-Based IV Method for Wideband System Identification

Cited by 16 publications

References 27 publications

Frequency‐coupled impedance model based subsynchronous oscillation analysis for direct‐drive wind turbines connected to a weak AC power system

Frequency‐coupled impedance model based subsynchronous oscillation analysis for direct‐drive wind turbines connected to a weak AC power system

Frequency‐coupled impedance model‐based sub‐synchronous interaction analysis for direct‐drive wind turbines connected to a weak AC grid

On the use of Frequency-Domain Subspace-based System Identification for Estimating Resonant Systems

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