Murat Caner, Chris Gerada, Greg Asher and Tolga Özer
The purpose of this paper is to investigate Halbach array effects in surface mounted permanent magnet machine (SMPM) in terms of both self-sensing and torque capabilities. A…
Abstract
Purpose
The purpose of this paper is to investigate Halbach array effects in surface mounted permanent magnet machine (SMPM) in terms of both self-sensing and torque capabilities. A comparison between a conventional SMPM, which has radially magnetized rotor, and a Halbach machine has been carried out.
Design/methodology/approach
The geometric parameters of the two machines have been optimized using genetic algorithm (GA) with looking Pareto. The performance of the machines’ geometry has been calculated by finite element analysis (FEA) software, and two parametric machine models have been realized in Matlab coupled with the FEA and GA toolboxes. Outer volume of the machine, thus copper loss per volume has been kept constant. The Pareto front approach, which simultaneously considers looks two aims, has been used to provide the trade-off between the torque and sensorless performances.
Findings
The two machines’ results have been compared separately for each loading condition. According to the results, the superiority of the Halbach machine has been shown in terms of sensorless capability compromising torque performance. Additionally, this paper shows that the self-sensing properties of a SMPM machine should be considered at the design stage of the machine.
Originality/value
A Halbach machine design optimization has been presented using Pareto optimal set which provides a trade-off comparison between two aims without using weightings. These are sensorless performance and torque capability. There is no such a work about sensorless capability of the Halbach type SMPM in the literature.
Details
Keywords
Murat Caner, Chris Gerada and Greg Asher
The purpose of this paper is to introduce a new design optimization technique for a surface mounted permanent magnet (SMPM) machine to increase sensorless performance at high…
Abstract
Purpose
The purpose of this paper is to introduce a new design optimization technique for a surface mounted permanent magnet (SMPM) machine to increase sensorless performance at high loadings by compromising with torque capability.
Design/methodology/approach
An SMPM parametric machine model was created and analysed by finite element analysis (FEA) software by means of the Matlab environment. Eight geometric parameters of the machine were optimized using genetic algorithms (GAs). The outer volume of the machine, namely copper loss per volume, was kept constant. In order to prevent sensorless performance loss at high loading, an optimization process was realized using two loading stages: maximum torque with minimum ripple at nominal load and maximum self-sensing capability at twice load. In order to show the effectiveness of the proposed technique, the obtained results were compared with the classical one-stage optimization realized for each loading condition separately.
Findings
With the proposed technique, fairly good performance results of the optimization were obtained when compared with the one-stage optimizations. Using the proposed technique, sensorless performance of the motor was highly increased by compromising torque capability for high loading. Additionally, this paper shows that the self-sensing properties of a SMPM machine should be considered at the design stage of the machine.
Originality/value
In related literature, design optimization studies for the sensorless capability of SMPM motor are very few. By increasing optimization performance, new proposed technique provides to achieve good result at high load for sensorless performance compromising torque capability.