Online Built-In Self-Test of High Switching Frequency DC–DC Converters Using Model Reference Based System Identification Techniques

Beohar, Navankur; Malladi, Venkata N.K; Mandal, Dipak Kumar; Ozev, Sule; Bakkaloglu, Bertan

doi:10.1109/tcsi.2017.2739479

Cited by 29 publications

(16 citation statements)

References 33 publications

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“…Figure 11b plots the difference between the theoretical model and the experimental results, and it confirms a good matching up to 30 kHz. Compared with other results shown in the literature [1,5,11,12], the noise in the frequency response is considerably low. Notice that the results in Figure 11a,b skip the smoothing process.…”

Section: Validation Of the Implemented Identification Systemcontrasting

confidence: 66%

“…Smoothing is applied to each segment separately, considering a different window length to apply the median. The length of the window is set by (11), where: L vector is the length of the data vector to which the smoothing process will be applied; N sg is the number of segments into which the data is divided; i is the segment number for which the number of points and window length are calculated, and w o is the starting window size of the moving median.…”

Section: Postprocessing Of the Measure: Smoothing Processmentioning

confidence: 99%

“…Smoothing process: it consists of the application of moving median and expressions ( 10) and (11) to the frequency response obtained in the block "Fourier analysis".…”

mentioning

confidence: 99%

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Nonparametric Frequency Response Identification for Dc-Dc Converters Based on Spectral Analysis with Automatic Determination of the Perturbation Amplitude

Granda

Fernández

Barrado

et al. 2021

Sensors

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Digital control for high switching frequency converter enables new features on DC-DC power conversion for a minimum cost. Frequency response identification is one such enabled functionality used in auto tunning, measurement of components to assess the converter’s state of health, or system stability monitoring. High accuracy, flexibility to operate in open or closed loop, and minimum impact in the converter’s regular operation are the frequency response identification system’s goals. We propose in this paper a nonparametric identification system addressing these main goals. First, it can autoadjust the perturbation size to reduce the perturbation’s impact on the converter’s output quantities. Second, as it is based on spectral analysis, it is suitable for open and closed-loop operation. Third, we demonstrate the identification system’s high accuracy, achieving a very low difference between the experimental measurements and the discrete model used as reference.

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Section: Validation Of the Implemented Identification Systemcontrasting

confidence: 66%

Section: Postprocessing Of the Measure: Smoothing Processmentioning

confidence: 99%

See 1 more Smart Citation

Nonparametric Frequency Response Identification for Dc-Dc Converters Based on Spectral Analysis with Automatic Determination of the Perturbation Amplitude

Granda

Fernández

Barrado

et al. 2021

Sensors

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“…Furthermore, the system behavior can be further investigated from the model, such as the overshoot, undershoot and steady-state operation. System identification techniques with parametric and non- [20,21]. Three models have been considered in this study via simulation, which are Auto Regressive with eXogenous (ARX), Auto Regressive Moving Average with eXogenous (ARMAX), and Output-error (OE) model structure.…”

Section: Introductionmentioning

confidence: 99%

Simulation and modeling of two-level DC/DC boost converter using ARX, ARMAX, and OE model structures

Amran

Bakar

Jalil

et al. 2020

IJEECS

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<span>This paper presents simulation and modeling of two-level DC/DC boost converter using system identification technique. The main objective is to identify the unknown mathematical model from designated converter that has 2 modes of operation. Signals from the converter were processed based on the impulse response from input and output voltages, which were in time domain data. Auto Regressive with eXogenous (ARX), Auto Regressive Moving Average with eXogenous (ARMAX), and Output-Error (OE) model structure techniques had been employed to generate a model from the converter, whose validation was based on coefficient of determination (R<sup>2</sup>) or best fits criterion. The result shows that the ARX model structure produced the best model with 94.03%, compared to ARMAX and OE with 93.70% and 92.25% respectively. In terms of stability for open-loop analysis, the ARX model structure gave the most stable system.</span>

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“…BIST technique uses closed-loop identification and model-based adaptive control techniques. [10][11][12] System identification and parameter estimation also find application in many mission-critical power electronics system where fault prognosis 13 and health monitoring 14 are required.…”

Section: Introductionmentioning

confidence: 99%

Closed-loop parametric identification of DC-DC converter

Padhee

Pati

Mahapatra

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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This study provides a step-by-step analysis of closed-loop parametric system identification for DC-DC buck converter. In closed-loop parametric identification, input-output experimental data are used to estimate the transfer function coefficients of DC-DC buck converter. For system identification purpose, a high-frequency perturbation signal is injected in to the closed-loop system which acts as an input signal for identification experiment. Different input-output models such as Auto-Regressive eXogenous, Auto-Regressive Moving Average with eXogenous, output error, and Box-Jenkins are used to model the converter structure and prediction error method is used to estimate the parameters. Model validation schemes are used to validate the estimated model. Simulation and experimental analysis have been provided to validate the results obtained.

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Online Built-In Self-Test of High Switching Frequency DC–DC Converters Using Model Reference Based System Identification Techniques

Cited by 29 publications

References 33 publications

Nonparametric Frequency Response Identification for Dc-Dc Converters Based on Spectral Analysis with Automatic Determination of the Perturbation Amplitude

Nonparametric Frequency Response Identification for Dc-Dc Converters Based on Spectral Analysis with Automatic Determination of the Perturbation Amplitude

Simulation and modeling of two-level DC/DC boost converter using ARX, ARMAX, and OE model structures

Closed-loop parametric identification of DC-DC converter

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