Application of averaged models to real-time monitoring of power converters

Allard, B.; Morel, Hervé; Ammous, Kaiçar; Lin-Shi, Xuefang; Bergogne, Dominique; Brevet, Olivier; Bevilacqua, Pascal

doi:10.1109/pesc.2001.954161

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…[12], uses an approximated expression of power loss in each phase of the converter to define the number of working phases, but this method is not really accurate. [7] and [13] propose to store the number of working phases for different operating points (output current, input voltage and duty cycle) of the converter in a look-up table. This approach is quite tedious as experimental tests are required for many operating points for each configuration (1, 2 or 3 phases) of the converter to establish this table.…”

Section: Multiphase Converters To Increase Power Range At High Efficimentioning

confidence: 99%

“…The approaches proposed by [13] and [15] are based on a precise analysis of the switching process. As these approaches are difficult to implement (typically measurements for different operating conditions of the converter cannot be automated), we propose to directly identify the total losses in one elementary converter without any distinction of switching, conduction and copper losses.…”

Section: Losses Modelingmentioning

confidence: 99%

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Accurate power loss model of a boost cell in a multiphase converter for phase management

Dupé

Jammes

Séguier

et al. 2014

2014 16th European Conference on Power Electronics and Applications

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This paper proposes a behavioral model of power losses for multiphase converter that will be used to optimize the efficiency of the converter by dynamically modifying the number of working phases. The aim of this work is to develop a simple (in terms of simulation resources) and but accurate (in terms of energy conversion) model of one cell of the multiphase converter that can be used to deduce the model of the whole converter, depending on the number of active phases. This model could also be used for long-time range simulations of photovoltaic systems.To build the model, a large number of tests has been performed on a multiphase converter at different values of the input voltage, the duty-cycle of the switch drive signals and the output currents. Experimental power losses of the boost cells have been compared with a model proposed in the literature. As the accuracy of this model was not satisfactory, we proposed to fit experimental data with feed-forward artificial neural networks.

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Section: Multiphase Converters To Increase Power Range At High Efficimentioning

confidence: 99%

Section: Losses Modelingmentioning

confidence: 99%

Accurate power loss model of a boost cell in a multiphase converter for phase management

Dupé

Jammes

Séguier

et al. 2014

2014 16th European Conference on Power Electronics and Applications

View full text Add to dashboard Cite

show abstract

“…As noted in [6], the power loss determinations during switching conditions and the parameters of the switching devices according to the datasheets [7] shows that the performance of switching components is not sufficiently accurate for such an application. The methods proposed by [8] and [9] are based on a precise analysis of the switching conditions. Because these methods are difficult to using and usually measurements cannot be obtained for the different operating conditions of the converter circuit, thus it is recommended to directly identify the total losses without any switching and conduction losses.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the switching losses in DC-DC boost converters by various machine learning methods

Sabancı

Balci

Aslan

2020

Journal of Energy Systems

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DC-DC converter circuits are topologies commonly used in power electronics applications such as renewable energy sources, electric vehicles, uninterruptible power supplies and DC transmission systems. The most important factors affecting efficiency and thus performance is the choice of the power semiconductor switching element as well as the circuit design and types of these topologies. In this context, power semiconductors are determined according to the switching frequency and current-voltage parameters. However, due to other operating modes of the circuit and load variation during the power conversion, the losses of the switching elements do not remain constant. In this study, a parametric simulation is performed in a conventional DC-DC boost converter circuit using the parameters related to the Insulated-Gate Bipolar Transistor (IGBT) power-switching element selected at a certain current-voltage capacity. These parameters are switching frequency, duty ratio and load change of the converter. Finally, using the data obtained, the loss of switching losses are estimated by the Multilayer Perceptron (MLP), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest (RF) Machine Learning (ML) techniques.

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“…The monitoring of their values is at a premium to ensure reliable operation. Extensive work and literature relate techniques based upon an estimator to avoid the cost and pitfalls of dedicated sensors [1][2][3]. The method proposed in this paper is based on the measurement of the drain source voltage at the terminals of one MOSFET in an inverter.…”

Section: Introductionmentioning

confidence: 99%

Towards a sensorless current and temperature monitoring in MOSFET-based H-bridge

Cyril

Bergogne

Herv

et al.

IEEE 34th Annual Conference on Power Electronics Specialist, 2003. PESC '03.

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-Chip temperature in power MOSFETs is commonly obtained (e.g. for thermal characterisation purposes) by feeding a known, controlled low DC-current in the body diode and measuring the forward-voltage drop.Drain current might be estimated by a voltage measurement across drain and source terminals, on-state resistance acting as a shunt, assuming that the temperature is known and constant.Such voltage-based measurements are attractive, but dependencies limit their application to laboratory environment. In industrial converters, neither temperature nor current are known a priori.Here we show that by combining the two techniques, previous limitations are overcome. Temperature and current come as two separate polynomial expressions.The application to a low-cost monitoring system is presented and results are discussed. The validity of the system over the full temperature and current ranges is verified and a confidence map is given.

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Application of averaged models to real-time monitoring of power converters

Cited by 8 publications

References 12 publications

Accurate power loss model of a boost cell in a multiphase converter for phase management

Accurate power loss model of a boost cell in a multiphase converter for phase management

Estimation of the switching losses in DC-DC boost converters by various machine learning methods

Towards a sensorless current and temperature monitoring in MOSFET-based H-bridge

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