Thermal analysis of a dry-type distribution power transformer using FEA

Arjona, M. A.; Hernández, C.; Escarela‐Perez, R.; Melgoza, E.

doi:10.1109/icelmach.2014.6960501

Cited by 10 publications

(9 citation statements)

References 16 publications

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“…Therefore, there is a heat transfer here, and this heat transfer occurs by conduction through the direct contact with the insulation material between the core and the coils, or by convection and radiation if there is an air gap between the core and the coils. Through the conduction, the heat flux, q, is proportional to the temperature gradient, T, and can be defined [7]:…”

Section: Modeling Of the Transformer Heat Transfermentioning

confidence: 99%

“…The power losses of the transformers are different for the no-load and loaded operation conditions. Since the current flowing through the secondary windings is zero for no-load condition, the losses are composing of major core losses and very small amount of winding losses [7]. However, as the transformer is loaded, the winding losses become higher.…”

Section: Introductionmentioning

confidence: 99%

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Thermal behavior of a three phase isolation transformer under load conditions with the finite element analysis

Balci

2020

THERM SCI

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Transformers are generally designed to operate under the sinusoidal excitation and the method called classical design method is used in design process. Nevertheless, they have to operate under the partly or fully non-linear excitation because of the increasing amount of the non-linear loads such as rectifiers, electric motor drivers, compact fluorescent lamps, computers, etc. Non-linear loads cause abnormal temperature rise both in the core and in the windings of the transformer which are designed for sinusoidal excitation. Nowadays in the virtual environment provided by the electromagnetic design software, transformers can be easily modeled with the finite element method for any type of non-linear loads or excitations. In this study, 3-D electromagnetic and thermal modeling of the isolation transformer at a certain rated power level have been carried out. Then, the core and the winding temperatures of the transformer have been comparatively reported under the linear and non-linear load conditions. Besides, forced air-cooling method of the transformer has been tested with the CFD. This study has shown that, transformer temperature can be kept in the safe operating region in any type of load by deciding the fan speed providing the required air-flow according to the transformer temperature.

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Section: Modeling Of the Transformer Heat Transfermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Thermal behavior of a three phase isolation transformer under load conditions with the finite element analysis

Balci

2020

THERM SCI

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“…Therefore, in the training of the neural network, it is important not to consider an average temperature. This is because during normal operation of the transformer, the temperature in a hot spot may increase excessively, to the point of damaging the equipment . So, naturally, in this work, for the training of the neural network, the considered temperature is the one with the hottest point in the transformer.…”

Section: Implementation Of the Proposed Toolmentioning

confidence: 99%

“…This is because during normal operation of the transformer, the temperature in a hot spot may increase excessively, to the point of damaging the equipment. 8,2 So, naturally, in this work, for the training of the neural network, the considered temperature is the one with the hottest point in the transformer. In turn, the hottest point was determined experimentally with the aid of a set of thermocouples during short-circuit tests.…”

Section: Temperature Networkmentioning

confidence: 99%

“…6 Therefore, estimating the value of the internal temperature (or the value of the temperature increase) is one of the most complex tasks in the transformer design. 3,7,8 Usually, transformers designers use either some design patterns curves or the thermal network method to estimate the temperature increase in the windings of this kind of equipment. Also, several studies have attempted to predict the internal temperature of the windings using empirical and mathematical models and computational intelligence techniques, which have obtained different degrees of success.…”

Section: Introductionmentioning

confidence: 99%

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Neural networks applied to the design of dry-type transformers: an example to analyze the winding temperature and elevate the thermal quality

Finocchio

Lopes

França

et al. 2016

Int. Trans. Electr. Energ. Syst.

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Summary Much of the cost of manufacturing transformers is related to the complexity in the development of its project, which involves many variables, such as different types of materials, methods, and manufacturing processes. Also, it is notoriously difficult to establish standards that relate the transformer characteristics with these design variables, since each, almost always, variable is calculated empirically. One of the most important design variables is the internal temperature of the transformers, which directly influences the lifetime of such equipments. So, a computational tool is under development whose purpose is to automate the design of cast resin dry‐type transformers and minimize the time taken for its completion, by means of artificial neural networks. In this article, we present some results of this tool, which relate some geometrical parameters of the specific transformer with the temperature of its windings. For this, the winding losses and total losses are also estimated. The training of the neural network was done with the test data of about 300 dry‐type transformers from the same manufacturer, so, with the same constructive features. Nevertheless, our results show that the technique is promising because the neural networks were well fitted and its results present errors lower than 1% when compared with data from tests with real transformers. Evidently, the proposed methodology is dependent on the constructive technique; that is, once trained, the neural network can be used only in the design of dry‐type transformers of the same constructive technique as those whose results of the tests were used to train the network. Nevertheless, even if only used to provide the initial parameters for a more complex design tool, the proposed method is useful since it is rapid and has low computational cost.

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Smart load management of distribution‐class toroidal transformers using a dynamic thermal model

Borbuev

Jazebi

et al. 2017

IET gener. transm. distrib.

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Thermal analysis of a dry-type distribution power transformer using FEA

Cited by 10 publications

References 16 publications

Thermal behavior of a three phase isolation transformer under load conditions with the finite element analysis

Thermal behavior of a three phase isolation transformer under load conditions with the finite element analysis

Neural networks applied to the design of dry-type transformers: an example to analyze the winding temperature and elevate the thermal quality

Smart load management of distribution‐class toroidal transformers using a dynamic thermal model

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