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PurposeThe purpose of this paper is to present the Moore-Penrose pseudoinverse (PI) modeling and compare with artificial neural network (ANN) modeling for switched reluctance machine (SRM) performance.Design/methodology/approachIn a design of an SRM, there are a number of parameters that are chosen empirically inside a certain interval, therefore, to find an optimal geometry it is necessary to define a good model for SRM. The proposed modeling uses the Moore-Penrose PI for the resolution of linear systems and finite element simulation data. To attest to the quality of PI modeling, a model using ANN is established and the two models are compared with the values determined by simulations of finite elements.FindingsThe proposed PI model showed better accuracy, generalization capacity and lower computational cost than the ANN model.Originality/valueThe proposed approach can be applied to any problem as long as experimental/computational results can be obtained and will deliver the best approximation model to the available data set.

CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior A aplicação de máquinas de relutância variável tem aumentado consideravelmente em aplicações industriais, sistemas de energia eólica e veículos elétricos. Isso ocorreu porque suas principais desvantagens, como a oscilação no torque, foram superadas devido à pesquisa contínua e suas vantagens, como construção simples e robusta, capacidade de operar em altas e variadas velocidades, insensibilidade a altas temperaturas e tolerância a faltas, tornaram a máquina de relutância variável a máquina certa para muitas aplicações. No entanto, algumas dificuldades são enfrentadas para executar bons projetos, como a não linearidade inerente do motor e a modelagem. Este trabalho explora a teoria, funcionamento e procedimentos de projeto nos primeiros capítulos. Em termos práticos é realizado a análise de sensibilidade do desempenho e a modelagem da máquina, ambos baseados na variação de parâmetros geométricos e nos resultados obtidos através de simulações de elementos finitos. O objetivo desse estudo é fornecer dados consistentes sobre quais dimensões devem ser alteradas para aplicações específicas, de modo a fundamentar escolhas feitas na etapa de projeto e otimização. Uma abordagem analítica utilizando a pseudoinversa de Moore-Penrose é proposta para modelar as características da máquina de relutância variável. No entanto, a modelagem proposta pode ser generalizada e utilizada para modelar qualquer sistema físico, desde que seja possível obter dados experimentais/computacionais a partir dele. Para organizar o conjunto de dados da modelagem e realizar a análise de sensibilidade foi utilizado métodos de planejamento de experimentos, que é um ramo da estatística aplicada utilizado para conduzir estudos científicos de um sistema, processo ou produto. Para atestar a qualidade da modelagem proposta um modelo utilizando rede neural artificial foi treinado com o mesmo conjunto de dados e seus resultados para predição de valores é comparado ao modelo pseudoinverso e aos valores obtidos por simulação de elementos finitos. Os resultados obtidos comprovam a viabilidade e qualidade do modelo proposto. The switched reluctance machine has gained much interest in industrial applications, wind power systems and electric vehicles. This happened because, its main disadvantages, such as the ripple in the torque, were overcome due to continuous research, and its advantages, such as simple and robust construction, ability to operate at high speeds and variable speeds, insensitivity to high temperatures and fault tolerance, have made the switched reluctance machine the right machine for many applications. However, some difficulties are faced to execute good designs, such as the inherent non-linearity of the machine and the modeling. This work explores the theory, operation and design procedures in the first chapters. In practical terms, performance sensitivity analysis and machine modeling is performed, both based on the variation of geometric parameters and the results obtained through finite element simulations. The purpose of this study is to provide consistent data on which dimensions must be changed for specific applications, in order to support choices made in the design and optimization stage. An analytical approach using the Moore-Penrose pseudo-inverse is proposed to model the characteristics of the switched reluctance machine. However, the proposed modeling can be generalized and used to model any physical system, as long as it is possible to obtain experimental/computational data from it. To organize the modeling data set and perform the sensitivity analysis, methods of design of experiments were used, which is a branch of applied statistics used to conduct scientific studies of a system, process or product. To certify the quality of the proposed modeling, a model using artificial neural network was trained with the same data set and its results for values prediction are compared to the pseudo-inverse model and the values obtained by finite element simulation. The results obtained prove the feasibility and quality of the proposed model. Tese (Doutorado)

The design of the switched reluctance machine involves several variables that impact the performance of the machine in different ways. The topology, polar arcs, internal and external diameter, number of turns and core length are some of the parameters that must be determined at the design stage. The effect of geometric parameters on performance is different for each switched reluctance machine topology, so this study is dedicated to establishing a sensitivity analysis for two popular topologies, a three-phase 6/4 and a four-phase 8/6. The performance results are estimated through finite element simulations and the dimensional effects analysis is performed using statistical software. The results show the dimensions with the greatest effect on the average torque value and total losses on the machine and can be useful to support the choices made during the design and optimization of this type of machine.

The main objective of modeling a switched reluctance machine is to derive a mathematical function to relate the outputs to the inputs. Due to the nonlinear relationship between the variables of torque, flux linkage, current and angular position of the rotor, Switched Reluctance Machine (SRM) modeling is a very challenging task and an open problem. Modeling is usually done in two situations, modeling a single machine, or modeling a set of machines. Each one must fulfill different requirements. This work presents a survey of different SRM modeling approaches, evaluating its advantages and limitations when modeling a single machine or a set of machines.

The proposal of this work is the study of the design of a three-phase distribution transformer and its optimization using techniques from natural differential evolution along with particle swarm optimization algorithms in order to minimize total losses and look at the inrush current. Transformer parameters were calculated analytically using the OCTAVE software, and the results obtained were used to define the geometry of the project and to carry out the following simulations: the first through the finite element method Finite Element Method Magnetics software, which analyzed the magnetic flux densities of the transformer core, and the second through Alternative Transient Program software for analyzing transformer inrush current. The analysis of the highest peak value of the energizing current of the three-phase transformer for the reference design and of the new optimized project is carried out by means of an approximate estimative analytical method applied to single-phase transformers and adapted in this research for use in three-phase transformers via analytical calculation and transient simulation. The verifications made through field results and measurements show that the methodology used for three-phase transformers is very efficient, minimizing losses with a consequent decrease in the energizing current.

This paper presents the application of two classes of Evolutionary Algorithms (EA) to determine optimum design of Single-Phase Switched Reluctance Machine (SPSRM). The EA used is Genetic Algorithms (GA) and Differencial Evolution (DE). Due to sensitivity of the output torque to the stator and rotor pole arcs, these are selected as design variables for a multi-objective optimization with the objective of maximizing average torque and torque density, and minimizing copper loss. The proposed optimization is tested on a 4/4 1,25 kW SPSRM, and the results of both algorithms are compared. The performance of the optimized motor are compared to the initial motor through the finite element analysis. The results show improvement in both efficiency and output torque.

The main objective of this work is to design a single-phase distribution transformer core type involved, with the help of optimization techniques to minimize total losses (core and windings). The optimization algorithms used are the differential evolution and particle swarm. His performances and results are compared. Moreover, it is detailed the importance of a good estimate of inrush current in transformers projects, its interference in security systems and electric power system in general. The main parameters of the project, the core dimensions, total losses, the no-load current and the transient magnetization current are computed through Octave software. These values are used to determine the geometry of the active part of the transformer in order to simulate the magnitude of flux density in the transformer core and the losses through finite element method using FEMM (Finite Element Method Magnetics) software. It was through the results the effectiveness of the methodology used, with reducing losses, improving the efficiency and significant decrease of the Inrush current.

In a design of a switched reluctance machine, there are a number of parameters that are chosen empirically inside a certain interval, therefore to find an optimum geometry it is necessary to determine how each parameter acts on the performance of the machine. This work presents a study on the influence of geometric dimensions on the performance of the switched reluctance machine. The analysis is done through finite element simulations based on the variation of one parameter while the others are fixed. Graphical and numerical results of torque and magnetic flux are presented for a 6/4 three-phase machine and an 8/6 four-phase machine. The study presented aims to provide consistent data on which dimensions should be modified for specific applications, and thus to base choices made in the design and optimization stage.

The article presents a review of the design procedures for a switched reluctance machine. A finite element simulation is used to obtain the operating data of the designed prototype. These data are flux linkage, inductance, torque and magnetic saturation. With them it is possible to profile and inductance torque and verification of design for the prototype. It also presents a criterion for analysis operational limit for the machine, based on data collected in the simulation.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior O uso de máquinas a relutância variável como uma alternativa para aplicações de velocidade variável e alta velocidades tem aumentado consideravelmente devido às suas vantagens como robustez, simplicidade de construção e baixo custo. As aplicações mais frequentes nos últimos anos estão relacionadas à aeronáutica, aos veículos elétricos e híbridos e à geração de energia eólica. Este trabalho explora a teoria, funcionamento, procedimentos de projeto e análise de uma máquina a relutância variável. Uma metodologia de projeto iterativo é introduzida e utilizada para o projeto de um protótipo de 1, 25. Para a análise da máquina dois métodos são utilizados, um método analítico e a simulação computacional de elementos finitos. Os resultados obtidos por ambos os métodos são comparados. Os resultados da simulação de elementos finitos são utilizados para determinar os perfis de indutância e torque do protótipo. A saturação magnética é analisada visualmente e numericamente em quatro pontos críticos da máquina. Os dados coletados na simulação permitem a verificação do projeto e dos limites de operação do protótipo. Além disso, é analisado o comportamento das grandezas de saída (indutância, torque e saturação magnética) através da variação das dimensões físicas do motor. Finalmente, uma otimização multiobjetivo, utilizando algoritmos de Evolução Diferencial e Algoritmos Genéticos, para o projeto da máquina a relutância variável é proposta. As variáveis otimizadas são os arcos polares do rotor e do estator, e os objetivos são maximizar o torque médio, o torque médio por perdas no cobre e o torque médio por volume do núcleo. Por fim, o projeto inicial e o projeto otimizado são comparados. Variable reluctance motors have been increasingly used as an alternative for variable speed and high speed drives in many industrial applications, due to many advantages like the simplicity of construction, robustness, and low cost. The most common applications in recent years are related to aeronautics, electric and hybrid vehicles and wind power generation. This paper explores the theory, operation, design procedures and analysis of a variable reluctance machine. An iterative design methodology is introduced and used to design a 1.25 kW prototype. For the analysis of the machine two methods are used, an analytical method and the finite element simulation. The results obtained by both methods are compared. The results of finite element simulation are used to determine the inductance profiles and torque of the prototype. The magnetic saturation is examined visually and numerically in four critical points of the machine. The data collected in the simulation allow the verification of design and operating limits for the prototype. Moreover, the behavior of the output quantities is analyzed (inductance, torque and magnetic saturation) by variation of physical dimensions of the motor. Finally, a multiobjective optimization using Differential Evolution algorithms and Genetic Algorithms for switched reluctance machine design is proposed. The optimized variables are rotor and stator polar arcs, and the goals are to maximize the average torque, the average torque per copper losses and the average torque per core volume. Finally, the initial design and optimized design are compared. Dissertação (Mestrado)