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The purpose of this study is to evaluate and minimize the losses of alternating current (AC) in the winding of electrical machines. AC winding losses are frequently disregarded at low frequencies, but they become a significant concern at high frequencies. This is the situation where applications require a high speed. The most significant applications in this category are electrical propulsion and drive systems.

An analytical model is used to predict the AC losses in the winding of electrical machines. The process involves dividing the slot into separate layers and then calculating the AC loss factor for each layer. The model aims to calculate AC losses for two different winding arrangements involving circular conductors. This application focuses on the stator winding of a permanent magnet synchronous motor that is specifically designed for electric vehicles. The model is integrated into an optimization process that makes use of the genetic algorithm method to minimize AC losses resulting from the arrangement of conductors within the slot.

This study and its findings demonstrate that the arrangement of the conductors within the slot has a comparable effect on the AC losses in the winding as the machine's geometric and physical properties. The effectiveness of electrical machines depends heavily on optimizing the arrangement of conductors in the slot to minimize AC winding losses.

The proposed strategy seeks to minimize AC winding losses in high-speed electric machines by providing a cost-effective and precise solution to improve energy efficiency.

This paper aims to present a novel multi-objective version of the Gorilla Troops optimizer (GTO), based on crowding distance, to achieve the optimal design of a brushless direct current motor.

In the proposed algorithm, the crowding distance technique was integrated into the GTO to perform the leader selection and also for the external archive refinement from extra non-dominated solutions. Furthermore, with a view to improving the diversity of non-dominated solutions in the external archive, mutation operator was used. For constrained problems, an efficient strategy was adopted. The proposed algorithm is referred to as CD-MOGTO.

To validate the effectiveness of the proposed approach, it was initially tested on three constrained multi-objective problems; thereafter, it was applied to optimize the design variables of brushless direct current motor to concurrently fulfill six inequality constraints, maximize efficiency and minimize total mass.

The results revealed the high potential of the proposed algorithm over different recognized algorithms in solving constrained multi-objective issues and the brushless direct current motors.

This study aims to improve the bat algorithm (BA) performance for solving optimization problems in electrical engineering.

For this task, two strategies were investigated. The first one is based on including the crossover technique into classical BA, in the same manner as in the genetic algorithm method. Therefore, the newly generated version of BA is called the crossover–bat algorithm (C-BA). In the second strategy, a hybridization of the BA with the Nelder–Mead (NM) simplex method was performed; it gives the NM-BA algorithm.

First, the proposed strategies were applied to solve a set of two standard benchmark problems; then, they were applied to solve the TEAM workshop problem 25, where an electromagnetic field was computed by use of the 2D non-linear finite element method. Both optimization algorithms and finite element computation tool were implemented under MATLAB.

The two proposed optimization strategies, C-BA and NM-BA, have allowed good improvements of classical BA, generally known for its poor solution quality and slow convergence rate.

This paper aims to deal with the development of a newly improved version of teaching learning based optimization (TLBO) algorithm.

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).

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.

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.

This study aims to focus on the application of the stochastic algorithms for optimal design of electrical machines. Among them, the authors are interested in particle swarm optimization and teaching–learning-based optimization.

The optimization process is realized by the coupling of the above methods to finite element analysis of the electromagnetic field.

To improve the performance of these algorithms and reduce their computation time, a coupling with the artificial neuron network has been realized.

The proposed strategy is applied to solve two optimization problems: Team workshop problem 25 and switched reluctance motor with flux barriers.

The modelling of the electromagnetic devices with moving objects such as launchers, electrical machines and actuators, necessitates considering the motion. This paper aims to examine this subject.

This task can be performed by introducing the velocity term in the electromagnetic equation. However, the application of this method leads to a non‐symmetrical finite element matrix. This numerical problem can be avoided either by the finite element meshing domain every displacement step or by using special techniques coupled to the finite element method like the moving bound, sliding surface and macro element (ME). The ME solution, based on an analytical model in the air‐gap of the devices, is more solicited for its low cost and accuracy by comparison with the other one. This technique keeps unchanged the finite element topology during the simulation, where the motion is taken into account by modification of the ME formula's every displacement.

This paper sought to present a new formula of the ME which is called dynamic ME. This new formula keeps unchanged the finite element topology and the terms of the analytical stiffness matrix too during the movement simulation.

The developed model is limited to analyzing the 2D devices with moving objects in linear or non‐linear case with saturation of the magnetic circuits. Extending the model to consider the 3D effects is the perspective of this work.

The developed formula is more economical than the classical one.

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.

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.

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.

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.