A Quasilinearization Based Algorithm for the Identification of Transient and Subtransient Parameters of Synchronous Machines

Jaleeli, N.; Bourawi, M. S.; Fish, John

doi:10.1109/mper.1986.5527771

Cited by 2 publications

(3 citation statements)

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“…Trajectory sensitivity method has been discussed in [48][49][50][51][52] to identify the model parameters using time series measurements of inputs and output signals. To calculate the trajectory sensitivities, explicit expressions could be derived in linear models.…”

Section: Parametric Methodsmentioning

confidence: 99%

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A review of power system dynamic equivalents for transient stability studies

Kai

Che

Zhang

et al. 2022

The Journal of Engineering

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The increasing complexity of interconnected power system has brought challenges to detailed dynamic simulation in terms of computational effort. Therefore, for transient stability assessment, dynamic equivalents of parts of large-scale system are required to capture the electromagnetic transients. This paper presents a review of past research on dynamic equivalencing using coherency-based methods and system identification methods. Future topics are discussed to improve the state-of-art approaches.

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Section: Parametric Methodsmentioning

confidence: 99%

“…To calculate the trajectory sensitivities, explicit expressions could be derived in linear models. For nonlinear models, linearization method is proposed in [51] to approximate sensitivities. In [53], a nonlinear model evaluation method from online measurements is proposed using trajectory sensitivities and least squares technique.…”

Section: Parametric Methodsmentioning

confidence: 99%

A review of power system dynamic equivalents for transient stability studies

Kai

Che

Zhang

et al. 2022

The Journal of Engineering

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“…However, the effects of noise on synchronous machine parameter estimation have not been studied extensively [11,12]. In reference I121 a quasilinearization-based least-square algorithm was used to estimate machine parameters from noise-corrupted data.…”

Section: Introductionmentioning

confidence: 99%

Identification of parameters of an AC machine from standstill time domain data

Keyhani

Moon

IEEE Workshop on Electronic Applications in Transportation

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This paper presents an evaluation of the performance of the maximum likelihood (ML) method when used to estimate the linear parameters of a synchronous machine model from the standstill time-domain flux decay test data. It is shown that a unique set of parameters can be obtained and the noise effects can be dealt with effectively when the ML estimation technique is used. The results of study also show that accurate machine parameters can be identified even when signal-to-noise ratio is as low as 200:l.

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A Quasilinearization Based Algorithm for the Identification of Transient and Subtransient Parameters of Synchronous Machines

Cited by 2 publications

References 0 publications

A review of power system dynamic equivalents for transient stability studies

A review of power system dynamic equivalents for transient stability studies

Identification of parameters of an AC machine from standstill time domain data

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