Optimal Design of Power Transformer Using Genetic Algorithm

Khatri, Ajay; Malik, Hasmat; Rahi, O. P.

doi:10.1109/csnt.2012.180

Cited by 34 publications

(21 citation statements)

References 7 publications

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“…In this paper a three-phase, three-legged core is examined. There are many advanced techniques described in the literature for core loss calculation [13][14][15][16]31]. Because of the GP modelling restrictions, the noload loss is calculated by a simple multiplication of the core mass and the power loss [W/kg].…”

Section: Active Part Modelmentioning

confidence: 99%

“…The authors have shown a general metaheuristic approach, which is based on the GP and capable to determine any core-form power transformer designs [25]. All of the reviewed methods [14], including the previously mentioned meta-heuristic optimisation method [25], are focused on the design of the active part of the transformer, which consists of the manufacturing price of the core and the windings, only. These models have a satisfactory level of accuracy in the absolute value of the TCO, hence these papers do not deal with the cost of the insulation oil and the radiator banks in the case of coreform power transformers.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore in the last decades, a wide range of mathematical optimisation methods have been implemented for this design optimisation problem, such as Monte Carlo simulation [18], artificial intelligence techniques and neural networks [19], evolutionary and genetic algorithms [13][14][15][16][17][18], and geometric programming (GP) [1,20,24,25]. The importance of GP is based on the recent developments in the solution methods, which can solve even large-scale GPs extremely efficiently and accurately [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

“…From the beginning of the 1950's many computer based algorithms have been introduced to determine the optimal key design parameters simplifying and accelerating the time-honoured, cut-and-try based design process [2,[13][14][15][16][17][18]. Therefore in the last decades, a wide range of mathematical optimisation methods have been implemented for this design optimisation problem, such as Monte Carlo simulation [18], artificial intelligence techniques and neural networks [19], evolutionary and genetic algorithms [13][14][15][16][17][18], and geometric programming (GP) [1,20,24,25].…”

Section: Introductionmentioning

confidence: 99%

“…The technique of capitalisation of the losses is used generally to take the economic aspects into consideration [7]. Because of the complexity and a lot of uncertainty in this task, several theories have been proposed to explain the capitalisation factors in the literature [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Impact of the Cooling Equipment on the Key Design Parameters of a Core–Form Power Transformer

Orosz

Tamus

2016

Journal of Electrical Engineering

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The first step in the transformer design process is to find the active part's key design parameters. This is a non-linear mathematical optimisation task, which becomes more complex if the economic conditions are considered by the capitalisation of the losses. Geometric programming combined with the method of branch and bound can be an effective and accurate tool for this task even in the case of core-form power transformers, when formulating the short-circuit impedance in the required form is problematic. Most of the preliminary design methods consider only the active part of the transformer and the capitalised costs in order to determine the optimal key design parameters. In this paper, an extension of this meta-heuristic transformer optimisation model, which takes the cost of the insulating oil and the cooling equipment into consideration, is presented. Moreover, the impact of the new variables on the optimal key design parameters of a transformer design is examined and compared with the previous algorithm in two different economic scenarios. Significant difference can be found between the optimal set of key-design parameters if these new factors are considered.

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Section: Active Part Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Impact of the Cooling Equipment on the Key Design Parameters of a Core–Form Power Transformer

Orosz

Tamus

2016

Journal of Electrical Engineering

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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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Introduction: Optimization and Metaheuristics Algorithms

Singh

Choudhary²

2020

Studies in Computational Intelligence

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Optimal Design of Power Transformer Using Genetic Algorithm

Cited by 34 publications

References 7 publications

Impact of the Cooling Equipment on the Key Design Parameters of a Core–Form Power Transformer

Impact of the Cooling Equipment on the Key Design Parameters of a Core–Form Power Transformer

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

Introduction: Optimization and Metaheuristics Algorithms

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