Cast-Resin Dry-Type Transformer Thermal Modeling Based on Particle Swarm Optimization

Azizian, Davood; Bigdeli, Mehdi

doi:10.1007/978-3-319-18296-4_12

Cited by 4 publications

(6 citation statements)

References 15 publications

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“…Consequently, the self-and mutual inductances satisfy the constraints of equalities (8) and (9). The identification of self-and mutual inductances was made using (7) such that the constraints shown in (8) and (9) are strictly satisfactory…”

Section: Implementation Of Ba To Estimate the Model Parametersmentioning

confidence: 99%

BA to construction of equivalent circuit of a transformer winding from frequency response analysis measurement

Chaouche¹,

Houassine²,

Moulahoum³

et al. 2018

IET Electric Power Applications

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The calculation of high-frequency model parameters of a winding is crucial for the integrity assessment of transformer winding damages and its severity. Bat algorithm (BA) is one of the modern and efficient methods used in computational intelligence due to its efficiency to obtain better results for complicated non-linear problems. In this study, an iterative method to ensure the mutually coupled equivalent model synthesis of an actual isolated transformer winding is proposed via bat search algorithm based on frequency response analysis data measurement (resonance frequencies, equivalent inductance and effective capacitance). The equivalent ladder network synthesis of transformer winding using the BA is based on the construction of the search space of the model parameters, thus each model element to be identified must be limited between two bounds (the upper and lower bounds) based on constraints and inequalities defining the model parameters. The effectiveness of the BA was tested by simulation and experimentally on two case studies of two isolated transformer windings with an air core. Furthermore, a comparative study of BA, particle swarm optimisation and the magnitude vector fitting was made in terms of accuracy and convergence speed.

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Section: Implementation Of Ba To Estimate the Model Parametersmentioning

confidence: 99%

BA to construction of equivalent circuit of a transformer winding from frequency response analysis measurement

Chaouche¹,

Houassine²,

Moulahoum³

et al. 2018

IET Electric Power Applications

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“…1b). In the transient condition, the transferred thermal energy to each unit appears as an increase in the total energy and the unit behaves as an integrated capacitor [16]. If only one unit is selected in the windings and if the heat transfer from horizontal surfaces is neglected (heat transfer is assumed to occur in the radial direction) [2], the thermal behaviour of the winding and the cooling air (on top of the enclosure [27]) can be explained as following [15]: …”

Section: Rc Thermal Modelmentioning

confidence: 99%

“…Different life-time and transient thermal models have been presented for oil-immersed transformers [9][10][11][12][13]. There are few researches on dynamic thermal modeling of the dry-type transformers [14][15][16][17]. References [14,15] introduce simplified RC models for the dynamic thermal modeling of the dry-type transformers.…”

Section: Introductionmentioning

confidence: 99%

“…As it has been shown in [15], the simplified models are accurate enough for the thermal modeling of the dry-type transformer. Additionally, some detailed RC thermal models have been presented in [16,17] for the thermal modeling of the cast-resin dry-type transformer. The RC models that are based on the physical structure of the transformer are accurate enough to analyse the dynamic thermal behaviour of the dry-type transformers.…”

Section: Introductionmentioning

confidence: 99%

“…Employing the measured temperatures, the ANN models have been trained and the predicted temperatures are validated against experimental results, the RC thermal models [15][16][17] and the classic IEC method [25]. Afterwards, using the trained ANN models, the life-time and reliability of the cast-resin transformer are studied.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A new cast-resin transformer thermal model based on recurrent neural networks

Azizian

Bigdeli

2017

Archives of Electrical Engineering

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Thermal modeling in the transient condition is very important for cast-resin dry-type transformers. In the present research, two novel dynamic thermal models have been introduced for the cast-resin dry-type transformer. These models are based on two artificial neural networks: the Elman recurrent networks (ELRN) and the nonlinear autoregressive model process with exogenous input (NARX). Using the experimental data, the introduced neural network thermal models have been trained. By selecting a typical transformer, the trained thermal models are validated using additional experimental results and the traditional thermal models. It is shown that the introduced neural network based thermal models have a good performance in temperature prediction of the winding and the cooling air in the cast-resin dry-type transformer. The introduced thermal models are more accurate for the temperature analysis of this transformer and they will be trained easily. Finally, the trained and validated thermal models are employed to evaluate the life-time and the reliability of a typical cast-resin dry-type transformer.

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Modeling and experimental validation of dry-type transformers with multiobjective swarm intelligence-based optimization algorithms for industrial application

Demirdelen

Esenboga

Aksu

et al. 2021

Neural Comput & Applic

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Cast-Resin Dry-Type Transformer Thermal Modeling Based on Particle Swarm Optimization

Cited by 4 publications

References 15 publications

BA to construction of equivalent circuit of a transformer winding from frequency response analysis measurement

BA to construction of equivalent circuit of a transformer winding from frequency response analysis measurement

A new cast-resin transformer thermal model based on recurrent neural networks

Modeling and experimental validation of dry-type transformers with multiobjective swarm intelligence-based optimization algorithms for industrial application

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