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The application of dry-type transformers is growing in the market because the technology is non-flammable, safer and environmentally friendly. However, the unit dimensions are normally larger and material costs become higher, as no oil is present for dielectric insulation or cooling. At designing stage, a transformer thermal model used for predicting temperature rise is fundamental and the modelling of cooling system is particularly important. This paper aims to describe a thermal model used to compute dry transformers with different cooling system configurations.

The paper introduces a fast-calculating thermal and pressure network model for dry-transformer cooling systems, preliminarily verified by analytical methods and advanced CFD simulations, and finally validated with experimental results.

This paper provides an overview of the network model of dry-transformer cooling system, describing its topology and its main variants including natural or forced ventilation, with or without cooling duct in the core, enclosure with roof and floor ventilation openings and air barriers. Finally, it presents a formulation for the new heat exchanger element.

The network approach presented in this paper allows to model efficiently the cooling system of dry-type transformers. This model is based on physical principles rather than empirical assessments that are valid only for specific transformer technologies. In comparison with CFD simulation approach, the network model runs much faster and the accuracies still fall in acceptable range; therefore, one is able to utilize this method in optimization procedures included in transformer design systems.

To provide first insight onto the application of hierarchical matrices and adaptive cross approximation (ACA) techniques for electromagnetic scattering problems.

The shielding effectiveness of metallic casings with apertures is analyzed via an electric field integral equation. To reduce the storage needs and the complexity of matrix equation solution, a technique combining the use of hierarchical matrices (H‐matrix) in conjunction with the ACA technique is used.

Provides first results for compression of a matrix resulting from a Helmholtz problem by means of hierarchical matrices and ACA techniques. Gives insight into the importance of obtaining a “cheap” preconditioner.

The technique resides on the smotheness of kernel functions – which is no longer valid for big wave numbers.

Gives means of solving problems of big dimensions in terms of number of unknowns – without the need to tailor the approach for the specific kernel function. The original integration functions used to fill the full matrix can be used here.

The paper represents one of the first attempts to use the above‐mentioned techniques for the high frequency domain.

The purpose of this paper is to extend a (μ/ρ, λ) evolution strategy to perform remarkably more globally and to detect as many solutions as possible close to the Pareto optimal front.

A C‐link cluster algorithm is used to group the parameter configurations of the current population into more or less independent clusters. Following this procedure, recombination (a classical operator of evolutionary strategies) is modified. Recombination within a cluster is performed with a higher probability than recombination of individuals coming from detached clusters.

It is shown that this new method ends up virtually always in the global solution of a multi‐modal test function. When applied to a real‐world application, several solutions very close to the front of Pareto optimal solutions are detected.

Stochastic optimization strategies need a very large number of function calls to exhibit their ability to reach very good local if not the global solution. Therefore, the application of such methods is still limited to problems where the forward solutions can be obtained with a reasonable computational effort.

The main improvement is the usage of approximate number of isolated clusters to dynamically update the size of the population in order to save computation time, to find the global solution with a higher probability and to use more than one objective function to cover a larger part of the Pareto optimal front.