Guidelines for Generator Stability Model Validation Testing

Hajagos, L.M.; Barton, J. R.; Berube, R.; Coultes, M. E.; Feltes, J.W.; Lanier, Gregory R.; Patterson, S.; Pereira, L.; Pourbeik, P.; Schneider, A.; Jones, Ron

doi:10.1109/pes.2007.386134

Cited by 10 publications

(4 citation statements)

References 31 publications

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“…The purpose of model validation is to ensure the correct performance of control systems and validate the computer models used for stability analysis [45]. Consequently, model parameters must represent adequately the dynamic performance of the device being modelled for power system studies [33].…”

Section: Renewable Power Generation Model Validationmentioning

confidence: 99%

“…There is considerable international experience regarding the validation of synchronous generator models [45,[47][48][49][50]. Validation methods are mainly based on staged tests, involving off-line tests such as enhanced short-circuit tests and partial load rejection tests [45], and frequency response testing covering Standstill Frequency Response (SSFR), Open Circuit Frequency Response (OCFR) and On Line Frequency Response (OLFR) described in [45,51]. Both processes take significant time and are expensive, and might be harmful for the generating units [50].…”

Section: Renewable Power Generation Model Validationmentioning

confidence: 99%

“…Then, parameters can be optimised through an automated iterative simulation approach. In addition, reactive power capability, excitation system and governor testing is also extensively described in literature [45].…”

Section: Renewable Power Generation Model Validationmentioning

confidence: 99%

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Current procedures and practices on grid code compliance verification of renewable power generation

Etxegarai

Eguia

Buigues

et al. 2017

Renewable and Sustainable Energy Reviews

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Section: Renewable Power Generation Model Validationmentioning

confidence: 99%

Section: Renewable Power Generation Model Validationmentioning

confidence: 99%

See 1 more Smart Citation

Current procedures and practices on grid code compliance verification of renewable power generation

Etxegarai

Eguia

Buigues

et al. 2017

Renewable and Sustainable Energy Reviews

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“…One of the most usual methods is to compare simulated results with its actual measurements after a disturbance. In general, there are three types of tests: offline test, open circuit test, and online test 4,26 . In offline and open circuit tests, the unit must be out of service and be in no‐load conditions, respectively.…”

Section: Introductionmentioning

confidence: 99%

Data mining based excitation system parameters estimation considering DCS and PMU measurements using cubature Kalman filter

Mohseni

Doroudi

Karrari

2020

Int Trans Electr Energ Syst

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Summary The excitation system is an important system of power plants, which has an effective role in power system dynamic and stability. Consequently, developing an approach for the excitation system's model and parameter estimation is a necessary research goal. In real situations, it is often needed to identify and estimate unknown parameters of the excitation system by field recorded signals. The recorded signal can be internal from the distributed control system (DCS) or external from the phasor measurement unit (PMU). In this article, a novel method based on cubature Kalman filter and data mining is proposed to identify the parameters of a standard type excitation system. Firstly, the best available signals for parameters estimation are chosen. Secondly, a method is proposed to parameters estimation of excitation systems efficiently, when both the DCS and PMU with different sample rates are employed to record the measurement data. Data mining is performed at intervals that DCS data is missing (or unavailable), while PMU data is available. Experimental data are used for validation of the proposed approach.

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