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The purpose of this paper is to propose a numerical procedure for the extraction of RL equivalent circuits of high frequency multi‐winding transformers with a low computational time.

Rigorous RL equivalent circuits of multi‐winding transformers can be obtained by performing open and short‐circuit tests. In this work, the finite element method (FEM) is employed as a virtual laboratory in order to derive such circuits. However, an accurate modeling of skin and proximity effects in the windings requires extremely dense meshes at high frequencies. Therefore, a 2D frequency‐domain homogenization of the windings, which conducts to coarser meshes, is applied in order to decrease the computational burden. The fine and homogenized models are compared in terms of simulation time as well as accuracy.

A significant decrease in simulation times is observed with the homogenized model (one order of magnitude at high frequencies for 2D models), while keeping acceptable relative error values (below 8 percent in the worst case, taking the fine model as reference). Furthermore, it is shown that the skin effect could contribute in a significant way to the total values of the circuit parameters, especially for high frequencies and for small fill factors. It should therefore not be neglected compared to the proximity effect when gathering such conditions, as commonly assumed in the literature.

Equivalent circuits which capture the skin and proximity effects are obtained at an acceptable computational cost, thanks to the use of homogenization techniques in FE simulations. To the best of the authors knowledge, such a procedure has not yet been published.

This paper explores through Schumacher's perspective on ‘the proper use of land’: the reasons for, and the means and consequences of, monitoring soil condition in managing agricultural landscapes sustainably. This particular perspective illustrates its argument with soil monitoring initiatives operating at various scales within the global agricultural context. Schumacher's land management goals are health, beauty and permanence, yet productivity is the goal most land managers focus on. The chosen indicators for soil monitoring need to reflect these goals. Hence, the indicators of choice for monitoring soil condition are attributes that can be: easily measured, improve soil productivity or protect the soil. Often attributes that have intrinsic ‘beauty’ (value), maintain ‘health’ (function) in ecosystems and are difficult to measure are ignored as soil condition indicators. The usefulness of information gained through monitoring soil condition is to make decisions that will be relevant for varied audiences and at different points in the decision-making process.

This paper presents a computer‐aided design (CAD) tool for the design of isolated dc‐dc converters.

This tool, developed in Matlab environment, is based on multiobjective optimization (MO) using genetic algorithms. The Elitist Nondominated Sorting Genetic Algorithm is used to perform search and optimization whereas analytical models are used to model the power converters. The design problem requires minimizing the weight, losses and cost of the converter while ensuring the satisfaction of a number of constraints. The optimization variables are, as for them, the operating frequency, the current density, the maximum flux density, the transformer dimensions, the wire diameter, the core material, the conductor material, the converter topology (among Flyback, Forward, Push‐Pull, half‐bridge and full‐bridge topologies), the number of semiconductor devices associated in parallel, the number of cells associated in series or parallel as well as the kinds of input and output connections (serial or parallel) of these cells. Finally, the design of an auxiliary railway power supply is presented and discussed.

The results show that such tool to design dc‐dc power converters presents several advantages. In particular, it proposes to the designer a set of solutions – instead of a single one – so that he can choose a posteriori which solution best fits the application under consideration. Moreover, interesting solutions not considered a priori can be found with this tool.

To the best of the authors’ knowledge, such a CAD tool including a MO procedure taking several topologies into account has not been suggested so far.

This paper gives a bibliographical review of the finite element and boundary element parallel processing techniques from the theoretical and application points of view. Topics include: theory – domain decomposition/partitioning, load balancing, parallel solvers/algorithms, parallel mesh generation, adaptive methods, and visualization/graphics; applications – structural mechanics problems, dynamic problems, material/geometrical non‐linear problems, contact problems, fracture mechanics, field problems, coupled problems, sensitivity and optimization, and other problems; hardware and software environments – hardware environments, programming techniques, and software development and presentations. The bibliography at the end of this paper contains 850 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1996 and 2002.

Diagnosing genetic neuromuscular disorder such as muscular dystrophy is complicated when the imperfection occurs while splicing. This paper aims in predicting the type of muscular dystrophy from the gene sequences by extracting the well-defined descriptors related to splicing mutations. An automatic model is built to classify the disease through pattern recognition techniques coded in python using scikit-learn framework.

In this paper, the cloned gene sequences are synthesized based on the mutation position and its location on the chromosome by using the positional cloning approach. For instance, in the human gene mutational database (HGMD), the mutational information for splicing mutation is specified as IVS1-5 T > G indicates (IVS - intervening sequence or introns), first intron and five nucleotides before the consensus intron site AG, where the variant occurs in nucleotide G altered to T. IVS (+ve) denotes forward strand 3′– positive numbers from G of donor site invariant and IVS (−ve) denotes backward strand 5′ – negative numbers starting from G of acceptor site. The key idea in this paper is to spot out discriminative descriptors from diseased gene sequences based on splicing variants and to provide an effective machine learning solution for predicting the type of muscular dystrophy disease with the splicing mutations. Multi-class classification is worked out through data modeling of gene sequences. The synthetic mutational gene sequences are created, as the diseased gene sequences are not readily obtainable for this intricate disease. Positional cloning approach supports in generating disease gene sequences based on mutational information acquired from HGMD. SNP-, gene- and exon-based discriminative features are identified and used to train the model. An eminent muscular dystrophy disease prediction model is built using supervised learning techniques in scikit-learn environment. The data frame is built with the extracted features as numpy array. The data are normalized by transforming the feature values into the range between 0 and 1 aid in scaling the input attributes for a model. Naïve Bayes, decision tree, K-nearest neighbor and SVM learned models are developed using python library framework in scikit-learn.

To the best knowledge of authors, this is the foremost pattern recognition model, to classify muscular dystrophy disease pertaining to splicing mutations. Certain essential SNP-, gene- and exon-based descriptors related to splicing mutations are proposed and extracted from the cloned gene sequences. An eminent model is built using statistical learning technique through scikit-learn in the anaconda framework. This paper also deliberates the results of statistical learning carried out with the same set of gene sequences with synonymous and non-synonymous mutational descriptors.

The data frame is built with the Numpy array. Normalizing the data by transforming the feature values into the range between 0 and 1 aid in scaling the input attributes for a model. Naïve Bayes, decision tree, K-nearest neighbor and SVM learned models are developed using python library framework in scikit-learn. While learning the SVM model, the cost, gamma and kernel parameters are tuned to attain good results. Scoring parameters of the classifiers are evaluated using tenfold cross-validation using metric functions of scikit-learn library. Results of the disease identification model based on non-synonymous, synonymous and splicing mutations were analyzed.

Certain essential SNP-, gene- and exon-based descriptors related to splicing mutations are proposed and extracted from the cloned gene sequences. An eminent model is built using statistical learning technique through scikit-learn in the anaconda framework. The performance of the classifiers are increased by using different estimators from the scikit-learn library. Several types of mutations such as missense, non-sense and silent mutations are also considered to build models through statistical learning technique and their results are analyzed.

To the best knowledge of authors, this is the foremost pattern recognition model, to classify muscular dystrophy disease pertaining to splicing mutations.

The purpose of this study is to develop a reliable finite element algorithm based on the transmission line method (TLM) to solve the nonlinear problem in electromagnetic field calculation.

In this paper, the TLM has been researched and applied to solve nonlinear iteration in FEM. LU decomposition method and the Jacobi preconditioned conjugate gradient method have been investigated to solve the equations in transmission line finite element method (FEM-TLM). The algorithms have been developed in C++ language. The algorithm is applied to analyze the magnetic field of a long straight current-carrying wire and a single-phase transformer.

FEM-TLM is more effective than traditional FEM in nonlinear iteration. The results of FEM-TLM have been compared and analyzed under different calculation scales. It is found that the LU decomposition method is more suitable for FEM-TLM because there is no need to repeatedly assemble the global coefficient matrix in the iterative solution process and it is not affected by the disturbance of the right-hand vector.

An effective algorithm is provided for solving nonlinear problems in the electromagnetic field, which can save a lot of computing costs. The efficiency of LU decomposition and CG method in FEM-TLM nonlinear iteration is investigated, which also makes a preliminary exploration for the research of FEM-TLM parallel algorithms.

This paper aims to omit the difficulties of directly finding the periodic steady-state solutions for electromagnetic devices described by circuit models.

Determine the discrete integral operator of periodic functions and develop an iterative algorithm determining steady-state solutions by a multiplication of matrices only.

An alternative method to creating finite-difference relations directly determining steady-state solutions in the time domain.

Reduction of software and hardware requirements for determining steady-states of electromagnetic.

A unified approach for directly finding steady-state solutions for ordinary nonlinear differential equations presented in the normal form.

Eliminate the necessity of solving high-order finite-difference equations for steady-state analysis of electromagnetic devices described by circuit models.

The purpose of this study is to identify a novel methodology for direct calculation of steady-state periodic solutions for electrical circuits described by nonlinear differential equations, in the time domain.

An iterative algorithm was created to determine periodic steady-state solutions for circuits with nonlinear elements in a chosen set of time instants.

This study found a novel differential operator for periodic functions and its application in the steady-state analysis.

This approach can be extended to the determination of two- or multi-periodic solutions of nonlinear dynamic systems.

The complexity of the steady-state analysis can be reduced in comparison with the frequency-domain approach.

This study identified novel difference equations for direct steady-state analysis of nonlinear electrical circuits.

This paper aims to introduce the decomposed harmonic balance finite element method (HBFEM) to decrease the memory requirement in large‐scale computation of the DC‐biasing magnetic field. Harmonic analysis of the flux density and flux distribution was carried out to investigate the DC biased problem in a laminated core model (LCM).

Based on the DC bias test on a LCM, the decomposed HBFEM is applied to accurately calculate the DC‐biasing magnetic field. External electric circuits are coupled with the magnetic field in the harmonic domain. The reluctivity matrix is decomposed and the block Gauss‐Seidel algorithm solves each harmonic solution of magnetic field and exciting current sequentially.

The calculated exciting currents and flux density are compared with that obtained from measurement and time domain finite element analysis, respectively, which demonstrates consistency. The DC bias leads to the significant saturation of the magnetic core and serious distortion of the exciting current. The flux density varies nonlinearly with DC bias excitation.

The harmonic balance method is only applicable in solving the steady state magnetic field. Future improvements in the method are necessary in order to manage the hysteresis effects in magnetic material.

The proposed method to solve the DC biased problem significantly reduces the memory requirement compared to the conventional HBFEM. The decomposed harmonic balance equations are solved efficiently by the block Gauss‐Seidel algorithm combined with the relaxation iterative scheme. An investigation on DC bias phenomena is carried out through the harmonic solution of the magnetic field. The decomposed HBFEM can also be applied to solve 3‐D DC‐biasing magnetic field and eddy current nonlinear problems in a practical power transformer.

The purpose of this paper is to investigate the influence of time steps, integration methods, and saturation modeling on the accuracy of the synchronous machine model. This model is compared with the PSCAD built‐in synchronous machine model in order to compare the accuracy of one of the most used synchronous machine models in a commercially available software versus a well‐documented and widely accepted state‐space synchronous machine model.

In the paper, a synchronous condenser with the saturation phenomenon is modeled using state‐space equations in the rotating dq‐reference frame and is implemented both in Matlab/Simulink and PSCAD. Integration methods of up to the fifth order are implemented for increased accuracy. The saturation modeling includes modeling of the saturation in both d‐ and q‐axis. A steady‐state and dynamic performance comparison towards the built‐in PSCAD synchronous machine model is performed. The saturation modeling does not include the saturation of the leakage fluxes.

When the forward Euler method is used, in order to obtain less than 5 percent error, the time step should not exceed 5 μs. The third‐order Runge‐Kutta method is the preferred choice and it provides desired accuracy when the time step is equal or smaller than 1,000 μs. The built‐in PSCAD model satisfies the error criteria for time steps smaller than 300 μs. A small discrepancy of 2 percent is found during the steady‐state test.

The paper presents the performance of the higher order integration methods in an EMTP‐type software environment where the trapezoidal integration method is most often used. It provides a good guide for building an owner‐defined model. A comparison of a dynamic performance between the publicly documented state‐space and a synchronous machine models commonly used for power system transient studies is presented.