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Purpose This paper aims to deal with the development of a newly improved version of teaching learning based optimization (TLBO) algorithm. Design/methodology/approach Random local search part was added to the classic optimization process with TLBO. The new version is called TLBO algorithm with random local search (TLBO-RLS). Findings At first step and to validate the effectiveness of the new proposed version of the TLBO algorithm, it was applied to a set of two standard benchmark problems. After, it was used jointly with two-dimensional non-linear finite element method to solve the TEAM workshop problem 25, where the results were compared with those resulting from classical TLBO, bat algorithm, hybrid TLBO, Nelder–Mead simplex method and other referenced work. Originality value New TLBO-RLS proposed algorithm contains a part of random local search, which allows good exploitation of the solution space. Therefore, TLBO-RLS provides better solution quality than classic TLBO.

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Purpose -Reducing eddy current losses in magnets of electrical machines can be obtained by means of several techniques. The magnet segmentation is the most popular one. It imposes the least restrictions on machine performances. This paper investigates the effectiveness of the magnet circumferential segmentation technique to reduce these undesirable losses. The full and partial magnet segmentation are both studied for a frequency range from few Hz to a dozen of kHz. To increase the efficiency of these techniques to reduce losses for any working frequency, an optimization strategy based on coupling of finite elements analysis and genetic algorithm is applied. The purpose of this paper is to define the parameters of the total and partial segmentation that can ensure the best reduction of eddy current losses. Design/methodology/approach -First, a model to analyze eddy current losses is presented. Second, the effectiveness of full and partial magnet circumferential segmentation to reduce eddy loss is studied for a range of frequencies from few Hz to a dozen of kHz. To achieve these purposes a 2-D finite element model is developed under MATLAB environment. In a third step of the work, an optimization process is applied to adjust the segmentation design parameters for best reduction of eddy current losses in case of surface mounted permanent magnets synchronous machine. Findings -In case of the skin effect operating, both full and partial magnet segmentations can lead to eddy current losses increases. Such deviations of magnet segmentation techniques can be avoided by an appropriate choice of their design parameters. Originality/value -Few works are dedicated to investigate partial magnet segmentation for eddy current losses reduction. This paper studied the effectiveness and behaviour of partial segmentation for different frequency ranges. To avoid eventual anomalies related to the skin effect an optimization process based on the association of the finite elements analysis to genetic algorithm method is adopted.

The aim of this study is to analyze and investigate some techniques to reduce eddy current losses in permanent magnets. First, a model using Maxwell's equations to analyze eddy current in permanent magnets is presented. Second, the effect of magnet circumferential segmentation on the eddy current losses in case of a surface mounted permanent magnets synchronous machine is investigated. A simple analysis of magnet demagnetization due to its eddy current losses is also studied. In a third step, second technique for reducing eddy current losses is presented. It relates to the stator slots skew, where the best skewing angle is obtained by means of an optimization process using genetic algorithm. A 2D nonlinear finite elements code is developed to achieve the purposes of the presented work.

Print ISBN: 978-1-4799-3786-8International audienceThis paper provides a new methodology for the characterization of defect size in a conductive nonmagnetic plate from the measurement of the impedance variations. The methodology is based on Finite Element Method (FEM) combined with the Multi Output Support Vector Machines (MO-SVM). The MO-SVM is a statistical learning method that has good generalization capability and learning performance. FEM is used to create the adaptive database required to train the MO-SVM and the Cross Validation (CV) is used to find the parameters of MO-SVM model. The results show the applicability of MO-SVM to solve eddy current inverse problems instead of using traditional iterative inversion methods which can be very time-consuming. With the experimental results we demonstrate the accuracy which can be provided by the MO-SVM technique

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