This paper proposes a comprehensive framework for multiobjective design optimization of switched reluctance motors (SRMs) based on a combination of the design of experiments and particle swarm optimization (PSO) approaches. First, the definitive screening design was employed to perform sensitivity analyses to identify significant design variables without bias of interaction effects between design variables. Next, optimal third-order response surface (RS) models were constructed based on the Audze-Eglais Latin hypercube design using the selected significant design variables. The constructed optimal RS models consist of only significant regression terms, which were selected by using PSO. Then, a PSO-based multiobjective optimization coupled with the constructed RS models, instead of the finite-element analysis, was performed to generate the Pareto front with a significantly reduced computational cost. A sample SRM design with multiple optimization objectives, i.e., maximizing torque per active mass, maximizing efficiency, and minimizing torque ripple, was conducted to verify the effectiveness of the proposed optimal design framework.

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Multiobjective Optimization of Switched ReluctanceMotors Based on Design of Experiments and ParticleSwarm Optimization

This paper focuses on the multiobjective design optimization of the permanent magnet synchronous linear motors (PMSLMs), which are applied to a high-precision laser engraving machine. A novel efficient multiobjective design optimization method for a PMSLM is proposed to achieve optimal performances as indicated by high average thrust, low thrust ripple, and low total harmonic distortion at different running speeds. First, based on the finite-element analysis (FEA) data, a regression machine learning algorithm, called an extreme learning machine (ELM), is introduced to solve the calculation modeling problem by mapping out the nonlinear and complex relationship between input structural factors and output motor performances. Comparative simulation experiments conducted using the traditional analytical modeling method and another machine learning modeling method, i.e., support vector machine, confirm the superiority of the ELM. Then, a new bionic intelligent optimization algorithm, called the gray wolf optimizer algorithm, is used to search the best optimization performances and structural parameters by performing iteration optimization calculation for multiobjective functions. Finally, FEA and prototype motor experiments prove the effectiveness and validity of the proposed method.

An Efficient Multiobjective Design Optimization Method for a PMSLM Based on an Extreme Learning Machine

In this article, a novel system-level robust design optimization method is presented to improve the performance of switched reluctance motor (SRM) drive systems under multiple operating conditions. Based on typical driving cycles of electric vehicles (EVs), five typical driving modes of the SRM are determined. The optimization objectives in each driving mode are established. The significant parameters of the motor and controller of each driving mode are selected as the optimization variables by using the sensitivity analysis. In order to simplify the optimization process, correlation analysis is performed to determine the coherence of the objective functions of all driving modes. Then, a sequential Taguchi method is applied to find an optimal design which is less sensitive to the noise factors. To verify the effectiveness of the proposed method, an SRM drive system applied in EVs with a 12/10 SRM and angle position control method is investigated. It is found that the proposed method can significantly reduce the torque ripple and improve the comprehensive performance. Finally, a 12/10 SRM is prototyped and tested to validate the simulation results.

System-Level Robust Design Optimization of a Switched Reluctance Motor Drive System Considering Multiple Driving Cycles

This article focuses on the local demagnetization fault recognition research of permanent magnet synchronous linear motor (PMSLM) and realizes the accurate identification of the position and degree of demagnetized permanent magnets. A fault recognition system based on S-transform (ST) and particle swarm optimization–least squares support vector machine (PSO–LSSVM) is proposed. The ST makes the induced electromotive force (EMF) signal with stronger signal characteristic expression ability, and the PSO–LSSVM model achieves better generalization ability and higher accuracy in the small sample state of PMSLM faults. In the process of fault identification: 1) the induced EMF analytical model for PMSLM under local demagnetization fault is presented; 2) the induced EMF signal is analyzed by ST, and the characteristic parameters are extracted from time–frequency curves. Then a characteristic vector is established by comparing the standard deviation values and similarities of different parameters; 3) a PSO–LSSVM classification model is established to realize the recognition of PMSLM faults. Prototype and finite element simulation experimental results confirm that the method can recognize the PMSLM faults accurately with a recognition rate of 100%.

Local Demagnetization Fault Recognition of Permanent Magnet Synchronous Linear Motor Based on S-Transform and PSO–LSSVM

Phase-shift or pole-shift method can reduce the detent force of the double-sided permanent magnet linear synchronous motor (DS-PMLSM) dramatically. However, this method causes a decrease in thrust and an increase in normal force simultaneously. To overcome this drawback, a novel slot-shift structure whose slots on the two sides of the primary component are staggered by an optimal distance is proposed in this paper. The models of the detent force including the end force and cogging force is established theoretically, and then verified by finite element analysis. The windings in the upper primary and the lower primary are arranged differently to improve the average thrust. The thrust characteristics of the DS-PMLSM under different shifted distances are analyzed and compared in detail. Furthermore, the primary component employing two kinds of core laminations is proposed to reduce both the dc component and ripple of the normal force. Finally, a DS-PMLSM prototype is tested, and the experimental results are compared with the analytical results to verify the effectiveness of the theoretical and simulation research.

Detent Force, Thrust, and Normal Force of the Short-Primary Double-Sided Permanent Magnet Linear Synchronous Motor With Slot-Shift Structure

This study focuses on optimisation design of permanent magnet linear synchronous motors that are applied in laser engraving machines with no cutting force. Traditional analytical optimisation method based on magnetic field with particle swarm optimisation algorithm was introduced to obtain the best combination of motor structure parameters. By contrast, the novel optimisation design method - Taguchi method based on orthogonal array was proposed to optimise the thrust and thrust ripple. After the design of experiments using finite-element analysis, the relative importance of each design parameter was estimated in detail. Experimental results of prototype can certify the superiority and validity of Taguchi optimisation method.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-epa.2016.0164

Optimal design of permanent magnet linear synchronous motors based on Taguchi method

The invention discloses a demagnetization detection apparatus and detection method of a permanent magnet synchronous linear motor. The demagnetization detection apparatus of a permanent magnet synchronous linear motor is characterized in that a linear motor with a demagnetization detection apparatus is used as a detection motor; the demagnetization detection apparatus takes an aluminium block as apedestal; a gaussmeter probe is fixedly arranged on the aluminium block by means of a clamp, so that the probe is arranged in the space air gap of the linear motor to be detected; adjusting bolts areused to adjust the positions of three detection points of the gaussmeter probe in space air gap of the linear motor to be detected; and the demagnetization detection apparatus is used for detecting distribution of air-gap flux density at the positions of the three detection points of the linear motor to be detected so as to realize demagnetization detection. The demagnetization detection apparatus and detection method of a permanent magnet synchronous linear motor can determine the difference distribution between the flux density to be detected and normal flux density distribution, and can extract the demagnetization fault characteristics. The demagnetization detection apparatus and detection method of a permanent magnet synchronous linear motor can realize accurate identification of thedemagnetization fault by means of a PNN classification algorithm.

Demagnetization detection apparatus and detection method of permanent magnet synchronous linear motor

When the image position measuring method is applied to mover position measurement of a linear motor, the selection of the target source image is crucial to measurement accuracy and anti-interference capacity. In this study, the fence image is used as the original shooting surface, and different kinds of fence images are constructed according to their width standard deviation and grey gradient sum. First, the relationship between the fence image spectrum energy and the correlation of image pixels is analysed. Then the image spectrum energy is used to characterise the feature of fence images during screening to select an aperiodic fence image with a strong anti-interference. Second, the phase singularity method is used to match the two images before and after displacement to obtain a sub-pixel displacement value of two images. Finally, according to the system calibration coefficient, the actual displacement value of the linear motor mover is obtained. Experimental results show that the fence image screening from image spectrum energy exhibits more anti-interference for the position measurement of the linear motor, and the measurement algorithm based on the phase singularity algorithm is more stable. The absolute error of displacement is <0.023 mm, and the relative error change within 0.054–0.862%.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-smt.2018.5354

Shaokun Dou

Papers

Optimal design of permanent magnet linear synchronous motors based on Taguchi method

Demagnetization detection apparatus and detection method of permanent magnet synchronous linear motor

Position measurement of linear motor mover based on image spectrum energy