Selected Nature-Inspired Algorithms in Function of PM Synchronous Motor Cogging Torque Minimisation

Cvetkovski, Goga; Petkovska, Lidija

doi:10.2478/pead-2021-0012

Cited by 7 publications

(4 citation statements)

References 37 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The definition of the cogging torque is quite simple and defined in the function of the basic motor parameters. Such a definition of the peak value of the cogging torque can be easily used as an objective function in an optimisation process (Cvetkovski and Petkovska, 2021). The simplified version of the formulation of the peak value of the cogging torque, which is the objective function of the optimisation, is presented by Eq.…”

Section: Cogging Torque -Single Objectivementioning

confidence: 99%

Design Improvement of Permanent Magnet Motor Using Single- and Multi-Objective Approaches

Cvetkovski,

Petkovska

2024

Power Electronics and Drives

Self Cite

View full text Add to dashboard Cite

Optimisation, or optimal design, has become a fundamental aspect of engineering across various domains, including power devices, power systems, and industrial systems. Engineers and academics have been actively involved in optimising these systems to achieve better performance, efficiency, and cost-effectiveness. Optimising electrical machines, including permanent magnet motors, is a complex task. It often involves solving intricate problems with various parameters and constraints. Engineers use different optimisation methods to tackle these challenges. Depending on the specific requirements and goals of a design project, engineers may employ either single-objective or multi-objective optimisation approaches. Single-objective optimisation focuses on optimising a single objective, while multi-objective optimisation considers multiple conflicting objectives. In optimisation, objective functions are mathematical representations of what needs to be optimised. In this case, optimising the efficiency of the motor, reducing cogging torque, and minimising the total weight of active materials are defined as possible objective functions. Genetic algorithms are nature based algorithms that are commonly used in engineering to find optimal solutions to complex problems, including those with multiple objectives. In this paper, after conducting optimisations using different objective functions and methods, a comparative analysis of the results is performed. This helps in understanding the trade-offs and benefits of different design choices. Finite element analysis (FEA) is a computational method used to analyse the physical properties and behaviours of complex structures and systems. In this case, FEA is used to validate and analyse selected optimisation solutions to ensure they meet the desired characteristics and parameters. Overall, this work demonstrates the interdisciplinary nature of engineering, where mathematics, computer science (for optimisation algorithms), and physics (for FEA) converge to improve the performance and efficiency of electrical machines. It also underscores the importance of considering multiple objectives in design processes to find optimal solutions that strike a balance between competing goals.

show abstract

Section: Cogging Torque -Single Objectivementioning

confidence: 99%

Design Improvement of Permanent Magnet Motor Using Single- and Multi-Objective Approaches

Cvetkovski,

Petkovska

2024

Power Electronics and Drives

Self Cite

View full text Add to dashboard Cite

show abstract

“…To reduce the computational cost, a circuit-like analytical model Cvetkovski and Petkovska (2021), amenable to the theory of PMSM will be used for analysis during the optimization procedure, while the convergence geometry will be re-analysed with a finite-element model for assessing the optimization results.…”

Section: The Case Studymentioning

confidence: 99%

Optimal shape design of a class of permanent magnet motors in a multiple-objectives context

Barba

Mognaschi

Petkovska

et al. 2022

COMPEL

Self Cite

View full text Add to dashboard Cite

Purpose This paper aims to deal with the optimal shape design of a class of permanent magnet motors by minimizing multiple objectives according to an original interpretation of Pareto optimality. The proposed method solves a many-objective problems characterized by five objective functions and five design variables with evolution strategy algorithms, classically used for single- and multi-objective (two objective functions) optimization problems. Design/methodology/approach Two approaches are proposed in the paper: the All-Objectives (AO) and the Many-Objectives (MO) optimization approach. The former is based on a single-objective optimization of a preference function, i.e. a normalized weighted sum. In contrast, in the MO a multi-objective optimization algorithm is applied to the minimization of a weight-free preference function and simultaneously to a maximization of the distance of the current solution from the prototype. The optimizations are based on an equivalent circuit model of the Permanent Magnet (PM) motor, but the results are assessed by means of finite element analyses (FEAs). Findings An extensive study of the solutions obtained by means of the different optimization approaches is provided by means of post-processing analyses. Both the approaches find non-dominated solutions with respect to the prototype that are substantially improving the initial solution. The points of strength along with the weakness points of each solution with respect to the prototype are analysed in depth. Practical implications The paper gives a good guide to the designers of electric motors, focussed on a shape design optimization. Originality/value Considering simultaneously five objective functions in an automated optimal design procedure is challenging. The proposed approach, based on a well-known and established optimization algorithm, but exploiting a new concept of degree of conflict, can lead to new results in the field of automated optimal design in a many-objective context.

show abstract

“…(d) Core overlap coefficient is 1. The groove torque expression can be obtained according to the energy method, i.e [5] α μ…”

Section: Analytical Expressionmentioning

confidence: 99%

Method of Cogging Torque Reduction for Built-in Permanent Magnet Synchronous Motor

Yang

Wang

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Cogging torque is one of the main factors that deteriorate the performance of permanent magnet motor. In order to reduce the cogging torque of built-in permanent magnet motor, a method of optimizing Rib and DminMag to reduce groove torque of built-in permanent magnet motor was proposed. Firstly, the relationship between Rib and DminMag and groove torque is analyzed through the expression of groove torque. Secondly, the optimal Rib and DminMag were found by finite element parametric calculation, and compared with the pre-optimization groove torque, the groove torque was significantly reduced. Finally, the main performance of motor before and after optimization is not significantly reduced.

show abstract

Selected Nature-Inspired Algorithms in Function of PM Synchronous Motor Cogging Torque Minimisation

Cited by 7 publications

References 37 publications

Design Improvement of Permanent Magnet Motor Using Single- and Multi-Objective Approaches

Design Improvement of Permanent Magnet Motor Using Single- and Multi-Objective Approaches

Optimal shape design of a class of permanent magnet motors in a multiple-objectives context

Method of Cogging Torque Reduction for Built-in Permanent Magnet Synchronous Motor

Contact Info

Product

Resources

About