DC-DC Buck Converter Parameter Identification Based on a White-Box Approach

Balakrishnan, Hariharan; Moreno-Ezuilaz, Manuel; Riba, Jordi-Roger; Bogarra, Santiago; García, A.

doi:10.1109/epepemc.2018.8521981

Cited by 12 publications

(9 citation statements)

References 6 publications

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“…Power converters may be represented as White-box, grey-box, or black-box model based on varying circumstances. facets [2].…”

Section: Introductionmentioning

confidence: 99%

Modelling of a DC-DC Buck Converter Using Long-Short-Term-Memory (LSTM)

Za’ter¹

2022

Preprint

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Artificial neural networks make it possible to identify black-box models. Based on a recurrent nonlinear autoregressive exogenous neural network, this research provides a technique for simulating the static and dynamic behavior of a DC-DC power converter. This approach employs an algorithm for training a neural network using the inputs and outputs (currents and voltages) of a Buck converter. The technique is validated using simulated data of a realistic Simulink-programmed nonsynchronous Buck converter model and experimental findings. The correctness of the technique is determined by comparing the predicted outputs of the neural network to the actual outputs of the system, thereby confirming the suggested strategy. Simulation findings demonstrate the practicability and precision of the proposed black-box method.

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“…Power converters may be represented as White-box, grey-box, or black-box model based on varying circumstances. facets [2].…”

Section: Introductionmentioning

confidence: 99%

Modelling of a DC-DC Buck Converter Using Long-Short-Term-Memory (LSTM)

Za’ter¹

2022

Preprint

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“…Currently, high-tech sectors such as aerospace, naval or automotive are based on complex power systems integrating multiple converters. Such complex power systems often consist of several SMPCs from various suppliers, which usually are reticent to provide detailed information of the internal components [6]. Therefore, habitually, the information available is not sufficient to generate detailed discrete models, although models which are too exhaustive may require unacceptable computational resources [7].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Least Squares Optimization for Parametric Identification of DC–DC Converters

Rojas-Dueñas

Riba

Moreno-Eguilaz

2021

IEEE Trans. Power Electron.

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Switching mode power converters are being extensively applied in different power conversion systems. Parameter identification comprises a set of techniques focused on extracting the relevant parameters of the converters in order to generate accurate discrete simulation models or to design enhanced condition diagnosis schemes. This paper applies a noninvasive optimization approach based on the non-linear least squares algorithm to determine the model parameters of different commercially available DC-DC power converters (buck, boost and buck-boost) from experimental data, including the parameters related to passive, parasitic and control loop elements. The proposed approach is based on a non-invasive on-line acquisition of the input/output voltages and currents under both steady state and transient conditions. The proposed method can also be applied to many other applications requiring precise and efficient parameter identification, including rectifiers, filters, or power supplies among others.

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“…This approach is applicable not only for identification of DC-DC converters but also for variety of applications such as filters and rectifiers. In [21] the parameters of the buck converter model were identified using a white-box model by obtaining data from the open-loop model and the closed-loop model.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Stability Analysis of DC-DC Power Electronic Systems by Means of Switching Equivalent Models

et al. 2021

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The proliferation of renewable energy sources is promoting the use of dc-dc power electronic converters in power distribution systems. It is well-known that the interconnection of power electronic converters can be a source of instabilities. Traditionally, impedance-based stability analyses are performed using either the analytical description or the identification of the output impedance of the source converter and the input admittance of the load converter. However, this methodology is restricted by the small-signal conditions, which are usually violated in this kind of systems due to the high variability imposed by the renewable energy sources. In nonlinear systems, the stability analysis usually consists on the definition of the region of attraction around a stable equilibrium point. In the literature, the analysis of nonlinear systems is almost exclusively applied to analytical systems, where all the details are known. This paper proposes a black-box methodology to obtain the region of attraction of the equilibrium point of commercial off-theshelf dc-dc converters working in power distribution systems. First, optimization algorithms are used to identify the parameters of a predefined structure, such that it is able to reproduce the dynamic behavior of the system. The parameters identified can be used to create a switching equivalent model that accounts for the nonlinearities produced by the switching process of the converters. Second, the bisection method is implemented to minimize the number of simulations needed to determine the region of stability accurately. The proposed methodology has been validated both with simulations and with an experimental setup.

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DC-DC Buck Converter Parameter Identification Based on a White-Box Approach

Cited by 12 publications

References 6 publications

Modelling of a DC-DC Buck Converter Using Long-Short-Term-Memory (LSTM)

Modelling of a DC-DC Buck Converter Using Long-Short-Term-Memory (LSTM)

Nonlinear Least Squares Optimization for Parametric Identification of DC–DC Converters

Nonlinear Stability Analysis of DC-DC Power Electronic Systems by Means of Switching Equivalent Models

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