A Methodology For Excitation Systems Identification

Castro, Héctor Antonio Botero; Scarpetta, José Miguel Ramírez

doi:10.1109/icieca.2005.1644350

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…Under laboratory conditions, experiments can be designed in such a way that the conditions of persistent excitation are met. This is typically achieved by applying a pseudorandom binary signal (PRBS) as input (Castro & Scarpetta, 2006; Soderstrom & Stoica, 1989). Applying this PRBS input under field conditions is often not possible, so one has to take the signals as measured.…”

Section: Discussionmentioning

confidence: 99%

Time‐variant modelling of heart rate responses to exercise intensity during road cycling

Lefever

Berckmans

Aerts

2012

European Journal of Sport Science

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The aim of this study was to determine if heart rate responses to training intensity during road cycling could be modelled with compact time-variant mathematical model structures. The model performance was evaluated in terms of model order (complexity), number of inputs and parameter estimation methods used (time-invariant vs. time-variant). Thirteen male cyclists performed two identical cycling tests of 27 km on the road. Uphill sections were introduced to induce dynamic variations in heart rate. The heart rate and training intensity, represented by power output and road inclination, were measured in real-time. Taking only power as system input allowed to explain the variations in heart rate in an accurate way R2 T = 0.86 ± 0.08, since adding the road inclination as an additional input did not significantly improve the modelling performance R2 T = 0.87 ± 0.08, P = 0.32. Furthermore, we demonstrated that models with first-order dynamics accurately describes the heart rate responses to power variations R2 T = 0.86 ± 0.08, but that more complex second-order model structures R2 T = 0.88 ± 0.08 were significantly better than the first-order model structures (P = 0.028). Finally, the heart rate dynamics appeared to be time-variant, since the time-variant model structures R2 T = 0.89 ± 0.07 were significantly better than the time-invariant model structures R2 T = 0.84 ± 0.08, P = 0.0002. So, compact time-variant second-order model structures could be used to model the heart rate response to training intensity as a basis for training optimisation.

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Section: Discussionmentioning

confidence: 99%

Time‐variant modelling of heart rate responses to exercise intensity during road cycling

Lefever

Berckmans

Aerts

2012

European Journal of Sport Science

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Analysis method on parameter identifiabilityfor excitation system model of generator

Liu

et al. 2014

2014 International Conference on Power System Technology

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The parameter identification methods, whichuse the experimental data to identify the parameters of the excitation system model, are widely used in the power systems. Although the model parameters obtained by these methods can properly fit experimental data, the identification results of some parameters may be unstable. To address this problem, this paper proposes a conception called sub-frequency domain sensitivity, which can provide a reliable index to assess whether the model parameters are easy to be identified or not for a nonlinear system. Based on this conception, a new parameter identification algorithm is proposed. In this algorithm, the existence of relevant parameters is judged by establishing the time domain sensitivity array of parameters at first, and then the identified parameters are divided into two categories: well-conditioned and ill-conditioned parameters. Based on the original ill-parameter group, evaluation representatives of the parameters are readjusted according to the sub-frequency domain sensitivity of parameters, finally, a "divide and rule" strategy is used to identify parameters. Case study is undertaken based on the IEEE ST2A type excitation system. Analysis results reveal that the proposed method can improve the accuracy and stability of parameter identification results in comparison with the traditional identification method based on time domain sensitivity.Index Terms-excitation system,parameter identification, sub-frequency domain sensitivity, related parameters, sensitivity matrix

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Investigation of Critical Fault Clearing Time by Applying Different Excitation System Models

Kastinen

Fuengwarodsakul

Wangdee

2019

2019 Research, Invention, and Innovation Congress (RI2C)

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A Methodology For Excitation Systems Identification

Cited by 5 publications

References 8 publications

Time‐variant modelling of heart rate responses to exercise intensity during road cycling

Time‐variant modelling of heart rate responses to exercise intensity during road cycling

Analysis method on parameter identifiabilityfor excitation system model of generator

Investigation of Critical Fault Clearing Time by Applying Different Excitation System Models

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