Design Optimization Of Induction Motor Research Articles

Design Optimization of Induction Motors with Different Stator Slot Rotor Bar Combinations Considering Drive Cycle

In this paper, a sequential Taguchi method for design optimization of an induction motor (IM) for an electric vehicle (EV) is presented. First, a series of empirical and mathematical relationships is systematically applied to reduce the number of possible stator slot rotor bar (SSRB) combinations. Then, the admissible optimal combinations are investigated and compared using finite element (FE) simulation over the NEDC driving cycle, and the three best combinations are selected for further analysis. Each topology is optimized over the driving cycle using the k-means clustering method to calculate the representative working points over the NEDC, US06, WLTP Class 3, and EUDC driving cycles. Then, using the Design of Experiment (DOE)-based Taguchi method, a multi-objective optimization is carried out. Finally, the performance of the optimized machines in terms of robustness against manufacturing tolerances, magnetic flux density distribution, mechanical stress analysis, nominal envelope curve and efficiency map is carried out to select the best stator slot rotor bar combination. It is also found that the K-means clustering method is not completely robust for the design of electric machines for electric vehicle traction motors. The method focuses on regions with high-density working points, and it is possible to miss the compliant with the required envelope curve.

Design Optimization of Induction Motor Using On-Line Improved Genetic Algorithms

The main objective of this work is the application of a new architecture of genetic algorithms to the induction machine design in order to improve their performance. The latter is proposed by our research team based on modified crossing and mutation operators who have fixed values for conventional genetic algorithms. In addition, this version is characterized by a double loop and a random crossover. Firstly, to demonstrate the ability to locate the global optimum with this version algorithm a mathematical function was used. Then we approached the second phase which its application in real time to the induction motor optimized design problem. Knowing that, the machine is a highly coupled with multivariable system and constraints. Finally, the results obtained have been analyzed where we have found that satisfactory and can be declared that adaptation algorithm is effective in locating rapidly the region in which the global optimum exists in relation to the classical genetic algorithm.

DESIGN AND PERFORMANCE EVALUATION OF THREE PHASE INDUCTION MOTOR USING OPTIMIZATION TECHNIQUES AND GENETIC ALGORITHMS

Optimization is usually done for any one of the functions associated with a system. In design problems usually a single objective function (say, Cost, Efficiency, Volume of Material used, Starting Current etc.) will be identified, which shall include the constraints imposed on the system also. Optimization can also be carried out with multiple objectives (say, Cost & Efficiency, Volume of Material & Stress handling capacity etc.), in which case, the problem becomes multi – objective programming problem and it can be solved by constructing an overall objective function as a linear combination of multiple objective functions. A research work is under progress on “Design and Performance Evaluation of Three Phase Induction Motor using Optimization Techniques and Genetic Algorithms”, which aims at developing a software, which upon providing minimum basic input data about the machine (such as overall dimensions, specification constraints etc) to be designed, designs the various parameters ably supported with suitable optimization techniques, to obtain an optimized end result. Induction motor design optimization is a problem in non-linear programming solved by the method of Sequential Unconstrained Minimization Technique (SUMT). Design Optimization is to be carried out using Hooke and Jeeves method of optimization and also using Genetic Algorithms.

Application of soft computing techniques to induction motor design

Purpose – The purpose of this paper is to present a comparative study of the various soft computing techniques and their application to optimum design of three‐phase induction motor design.Design/methodology/approach – The need for energy conservation is increasing the requirements for increased efficiency levels of induction motor. It is therefore important to optimize the efficiency of induction motor in order to obtain significant energy savings. To optimize the efficiency, design of the induction motor has to be chosen appropriately. In this paper, computational intelligence techniques such as artificial neural network, fuzzy logic, genetic algorithm, differential evolution, evolutionary programming, particle swarm optimization, simulated annealing approach, radial basis function, and hybrid approach are applied to solve the induction motor design optimization problem.Findings – These methods are tested on two sample motors and the results are compared and validated against the conventional Modified H...

Design Optimization of Induction Motor by Genetic Algorithm and Comparison with Existing Motor

This paper presents an optimal design method to optimize three-phase induction motor in manufacturing process. The optimally designed motor is compared with an existing motor having the same ratings. The Genetic Algorithm is used for optimization and three objective functions namely torque, efficiency, and cost are considered. The motor design procedure consists of a system of non-linear equations, which imposes induction motor characteristics, motor performance, magnetic stresses and thermal limits. Computer simulation results are given to show the effectiveness of the proposed design process.

Induction motor design for electric vehicle using a niching genetic algorithm

In the case of the shape or structural optimization of induction motor design, it is necessary to identify multiple optimal profiles by locating local optima as well as global. Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions. In this paper, optimal design of an induction motor for an electric vehicle using a niching method adopting restricted competition selection is proposed. The evaluation criteria and the standards of the best design selection are also presented.

Erratum: An SQP Algorithm for Finely Discretized Continuous Minimax Problems and Other Minimax Problems with Many Objective Functions

Previous article Full AccessErratum: An SQP Algorithm for Finely Discretized Continuous Minimax Problems and Other Minimax Problems with Many Objective FunctionsJian L. Zhou and André L. TitsJian L. Zhou and André L. Titshttps://doi.org/10.1137/S1052623496313659PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstractAn error is pointed out in the local convergence proof in the quoted paper [J. L. Zhou and A. L. Tits, SIAM J. Optim., 6 (1996), pp. 461--487]. A correct proof is given.[1] S. M. Robinson, Perturbed Kuhn–Tucker points and rates of convergence for a class of nonlinear‐programming algorithms, Math. Programming, 7 (1974), pp. 1–16. mhp MHPGA4 0025-5610 Math. Program. CrossrefGoogle Scholar[2] Jian Zhou and , André Tits, An SQP algorithm for finely discretized continuous minimax problems and other minimax problems with many objective functions, SIAM J. Optim., 6 (1996), 461–487 97a:90101 LinkISIGoogle ScholarKeywordscontinuous minimaxsemi-infinite programmingmany constraintssequential quadratic programmingdiscretizationglobal convergence Previous article FiguresRelatedReferencesCited byDetails A box moments approach for the time-variant hybrid reliability assessment14 September 2021 | Structural and Multidisciplinary Optimization, Vol. 64, No. 6 Cross Ref SIPAMPLACM Transactions on Mathematical Software, Vol. 30, No. 1 Cross Ref Design optimization of induction motors for aerospace applications Cross Ref Volume 8, Issue 1| 1998SIAM Journal on Optimization History Published online:31 July 2006 InformationCopyright © 1998 Society for Industrial and Applied MathematicsKeywordscontinuous minimaxsemi-infinite programmingmany constraintssequential quadratic programmingdiscretizationglobal convergenceMSC codes49M0749M3749M3965K0590C0690C3090C34PDF Download Article & Publication DataArticle DOI:10.1137/S1052623496313659Article page range:pp. 284-285ISSN (print):1052-6234ISSN (online):1095-7189Publisher:Society for Industrial and Applied Mathematics

A new method for the design optimization of three-phase induction motors

The paper deals with the optimization problem of induction motor design. In particular a new global minimization algorithm is described: it tries to take into account all the features of these particular problems. A first numerical comparison between this new algorithm and a method widely used in the design optimization of induction motors has been performed. The obtained results show that the proposed approach is promising.

Error-based global optimization approach for electric motor design

This paper proposes a new global optimization approach for electric machine design problems. The algorithm uses an error function between the actual objective function, at a given computational step, and its limit value to search for the global minimum. The error function provides a good indication of how far or close the objective function approaches its ultimate solution. This method is considered to be self adaptive in a sense that the higher the error is, the larger the step size of the variables will be. This feature helps the search to escape local solutions and to reach eventually the global solution. The proposed method was tested on various typical multimodal functions and particularly applied to induction motor design optimization problems. This algorithm is very simple and easy to be implemented, yet powerful and effective as indicated by the test results.

Application of fuzzy decision to optimization of induction motor design

This paper describes a maximizing decision making scheme employing fuzzy set theory for the optimal design of induction motors. The fuzzy maximizing decision making is combined with a motor synthesis method, which can reflect the designer's experience, view and judgment. The proposed algorithm is applied to a multiobjective optimization of an induction motor design.

Analysis of electromagnetic and thermal fields for induction motors during starting

In order to improve the starting performance of squirrel-cage induction motors, the so-called skin effect becomes very serious, which causes a highly uneven distribution of rotor-bar currents. The corresponding conduction losses of the upper and lower portions of rotor bars create high thermal stress, which may damage the rotor bars, particularly at the junction of rotor bars and end-rings. Hence, it is important to predict the starting and operating characteristics of induction motors more accurately for the purpose of ensuring the reliability of motor operation. This paper employs a visual-aided finite-element method to analyze the electromagnetic and thermal fields of induction motors during starting. By using visual and interactive pre- and post-processing techniques, the analysis can be applied to squirrel-cage induction motors with any physical dimensions, material properties, and system parameters. The starting performance, including the distributions of magnetic flux density, current density, loss density and temperature rise, are presented dynamically by computer animation, which are convenient and powerful for the evaluation and design optimization of induction motors. The theoretical predictions are verified by direct comparison with experimental results. >

Optimization of Three-Phase Induction Motor Design Part I: Formulation of the Optimization Technique

This two-part paper deals with the optimization of the induction motor designs with respect to cost and efficiency. Most studies on the design of an induction motor using optimization techniques are concerned with the minimization of the motor cost and describe the optimization technique that was employed, giving the results of a single (or several) optimal design(s). In the present paper, a more comprehensive study on the optimization of a three-phase induction motor design was performed. This includes the relationship between motor cost, efficiency, and power factor; the effect of the properties of the electrical steel; and other effects as they occur in an optimal design. In addition, the optimization procedure that was used in this paper includes a design program, where some of the secondary parameters (which are called here variable constants), are modified according to the optimal results, in contrast to other studies where these parameters remain constant for the entire optimization. In this part, a new mathematical formulation of the optimization problem of the induction motor is presented.

Experience in Design Optimization of Induction Motor Using 'SUMT' Algorithm

Design optimization of an induction motor is considered as a nonlinear multivariable constrained programming problem. A set of nine basic variables is identified and suitable constraints are imposed to meet the thermal, starting and other performance requirements of the machine. Six different objective functions are considered to facilitate the selection of suitable optimized designs for any given application. The optimization is achieved through Rosenbrock's method of direct search in conjunction with the sequential unconstrained minimization technique (SUMT). For a typical 3.7kw cage motor, the optimized design results with different objective functions are presented.

Experience in Design Optimization of Induction Motor Using 'SUMT' Algorithm

Experience in Design Optimization of Induction Motor Using 'SUMT' Algorithm

Induction motor design optimization using constrained Rosenbrock method (hill algorithm)

In the present work an optimum design of polyphase cage induction motor using Rosenbrock's method is put forth. The optimization programme considers the cost of active materials as an objective function and temperature rise and other performance items as constraints. The variables chosen are ampere conductors/m, ratios of stack length to pole pitch and stator slot depth to width, stator core depth (cm), average air gap flux density (wb/m 2) and stator and rotor winding current densities (A/mm 2).

Computer-Aided Synthesis and Optimization of Induction Motor Design

A method of computer-aided design of induction motors is proposed. Synthesis of a feasible design is achieved by use of the digital computer operating in an interactive mode, the objective being to utilize both man and machine to their best advantage. Optimization of the feasible design is accomplished automatically by using a nonlinear function maximization algorithm. The method has been proved by producing a design to an exacting specification and then using the optimized design details to build an experimental motor. This machine exceeded the specified requirements in every respect; its theoretical performance was, in fact, 9.8 percent than the specification, according to a merit factor criterion proposed by the authors in an attempt to quantify good design. The method is economically justifiable because better designs are produced quickly and cheaply and, once the relevant analytical subroutines have been developed, virtually any type of electrical machine may be designed in the fashion described.

A Comparative Study of Minimization Techniques for Optimization of Induction Motor Design

In a previous paper induction motor design optimization was formulated as a problem in nonlinear programming and the method of Sequential Unconstrained Minimization Technique (SUMT) was used to obtain the optimum design. The steepest descent method was used to obtain the minimum. In this paper details of a comparative study of five different unconstrained minimization techniques are given. Both indirect and direct search type methods are used in conjunction with SUMT. It has been found that direct search methods are specially suited for the types of functions occuring in rotating machine design problems.

Optimization Of Pplyphase Induction Motor Design: A Nonlinear Programming Approach

The problem of optimization of induction motor design is tackled as a nonlinear programming problem. The objective function or the cost function is formed taking only the material cost into account. The specifications like pull out torque, starting torque etc. are put as constraint functions. These functions are expressed in terms of important dimensions of the machine which are assumed to be continuously variable. The sequential unconstrained minimization technique for nonlinear programming is used to obtain an optimum design. Analysis and synthesis programs needed for the problem have also been developed. The method is applied to a line of small single cage integral horsepower motors.