Assessment of model parameters to identify an equivalent wind power plant

Cari, Elmer P. T.; Alberto, Luís; Erlich, I.

doi:10.1109/ptc.2015.7232472

Cited by 9 publications

(9 citation statements)

References 7 publications

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“…Based on the parameter values found in Cari et al (2015), the search region used in MVMO was defined, as displayed in Table 1. At every generation, three parameters of the fittest individual in the population would suffer mutation in order to generate one new individual.…”

Section: Resultsmentioning

confidence: 99%

“…The data used in the estimation process adressed in this paper was the same used in Cari et al (2015), where the authors apply only the Trajectory Sensitivity Method to estimate the parameters of the model presented in this section.…”

Section: Collected Datamentioning

confidence: 99%

“…In Erlich et al (2012), a generic model of equivalent wind power plant was introduced and its parameters were estimated by a metaheuristc known as Mean-Variance Mapping Optimization (MVMO). In Cari et al (2015), Trajectory Sensitivity Method (TS), a nonlinear method, was applied to estimate the parameter of the same generic model.…”

Section: Introductionmentioning

confidence: 99%

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Parameter Estimation of Wind Power Plant Equivalent Model through a Hybrid Method

Gomes

Cari

2020

Anais Do Congresso Brasileiro De Automática 2020

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Reliable models are vital for dynamic simmulations made by electric system operators. Generic models can be adjusted to match certain equipment criteria and provide accurate responses to different faults and disturbances. This paper adresses that issue by proposing a hybrid estimation method to estimate parameters of a wind power plant generic model. At first, the parameters are estimated through the Mean-Variance Mapping Optimization method, a population-based metaheuristic. When the parameters are close enough to their real values, Trajectory Sensitivity method is applied to improve the results and optimize solution. Combining both methods results in a fast and robust estimation approach. The proposed hybrid method was executed using measurement data acquired from PowerFactory and the results show the adequacy of this application.

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Section: Resultsmentioning

confidence: 99%

Section: Collected Datamentioning

confidence: 99%

See 1 more Smart Citation

Parameter Estimation of Wind Power Plant Equivalent Model through a Hybrid Method

Gomes

Cari

2020

Anais Do Congresso Brasileiro De Automática 2020

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“…When identifying the model's parameters, the theoretical model with the experimental data are usually combined to identify the parameter value [19][20][21][22][23][24][25][26]. As an essential method of parameter identification, the optimization algorithm has a strong search performance for a specific parameter range, and it has high adaptability for practical problems [19,21,23,24]. In addition, it can visualize the performance of identification results through the value of the fitness function.…”

Section: Parameter Identification Based On Optimization Algorithmmentioning

confidence: 99%

Synchronous Machine Winding Modeling Method Based on Broadband Characteristics

2021

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The accurate establishment of the equivalent circuit model of the synchronous machine windings’ broadband characteristics is the basis for the study of high-frequency machine problems, such as winding fault diagnosis and electromagnetic interference prediction. Therefore, this paper proposes a modeling method for synchronous machine winding based on broadband characteristics. Firstly, the single-phase high-frequency lumped parameter circuit model of synchronous machine winding is introduced, then the broadband characteristics of the port are analyzed by using the state space model, and then the equivalent circuit parameters are identified by using an optimization algorithm combined with the measured broadband impedance characteristics of port. Finally, experimental verification and comparison experiments are carried out on a 5-kW synchronous machine. The experimental results show that the proposed modeling method identifies the impedance curve of the circuit parameters with a high degree of agreement with the measured impedance curve, which indicates that the modeling method is feasible. In addition, the comparative experimental results show that, compared with the engineering exploratory calculation method, the proposed parameter identification method has stronger adaptability to the measured data and a certain robustness. Compared with the black box model, the parameters of the proposed model have a certain physical meaning, and the agreement with the actual impedance characteristic curve is higher than that of the black box model.

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“…Instead, it should be based on measurements at the PCC. Thus, parameter identification method based on PCC measurements for single‐machine equivalent model considering network layout inside WPP is emerged [11–13], as it is easy to derive the terminal voltage and power of the single‐machine equivalent model from the PCC measurements, while when it comes to multi‐machine equivalent model, it is impractical to identify all the parameters of multi‐machine at the same time due to the parameter sensitivity to the measurements.…”

Section: Introductionmentioning

confidence: 99%