Multi-objective optimization design and performance evaluation of slotted Halbach PMSM using Monte Carlo method

Bouloukza, Ibtissam; Mordjaoui, Mourad; Medoued, Ammar; Soufi, Youcef

doi:10.24200/sci.2017.4361

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…In electrical machines design, numerical methods such as finite element analysis (FEA) have been generally used for shape optimization [12], and torque improvement [13]. Even though the analysis can be achieved by the numerical methods, the analytical solutions can facilitate the optimal design procedure [14,15].…”

Section: Introductionmentioning

confidence: 99%

Analytical Study, Design and Optimization of Radial-Flux PM Limited-Angle Torque Motors

Yazdanpanah

2020

Scientia Iranica

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This paper presents an analytical study of the radial-flux slotless limited-angle torque motors. The modelling is based on the magnetic equivalent circuit of the actuators and uses the electromagnetic equations to calculate the air-gap flux as well as the produced torque. This model is then used for designing the actuators both in outer-rotor and innerrotor structures, considering the design constraints and desired characteristics. As the objectives in design stage usually conflict with each other, an intelligent multi-objective optimization algorithm is required to design the best fit actuators. Analytical and simulation results are presented and compared to show the accuracy of the model and verify the design equations as well as the design approach. As this type of actuators is a key element in industrial control, the contributions of this paper are focused on new analytical field solution based on magnetic equivalent circuit, design and optimization approach for radial-flux structure and introducing a general procedure that could be extended to similar structures and actuators.

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Section: Introductionmentioning

confidence: 99%

Analytical Study, Design and Optimization of Radial-Flux PM Limited-Angle Torque Motors

Yazdanpanah

2020

Scientia Iranica

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“…In [17], authors use a multi-physics model comprising six sub-models that are electrical, magnetic, thermal, mechanical, geometrical, and economical to design an optimum 55kW PMSG. Several optimization methods are presented in the literature for designing PM synchronous machines such as Sequential-Stage strategy [18], Interactive multi-objective [19], Monte Carlo [20], and Multi-Objective Genetic Algorithm [21].…”

Section: Introductionmentioning

confidence: 99%

Design and Optimization of a Large-Scale Permanent Magnet Synchronous Generator

et al. 2019

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Direct drive permanent magnet synchronous generators have numerous advantages such as improved reliability, low maintenance, long life, and developed performance characteristics. In recent years, many researchers have worked on this generator to enhance the performance of the generator, especially for the wind turbine application. The focus of this paper is on the development of a step-by-step method for the design of a permanent magnet synchronous generator. Then the winding function method is used to model the generator and calculate its output characteristics analytically. The analytical results of the designed generator are validated using the finite element analysis (FEA) and it is demonstrated that the obtained results from both methods are in great agreement with the experimental measurements of the Northern Power direct-drive generator. The sensitivity analysis and optimization procedure based on the genetic algorithm are used to design an optimum generator. The optimization goal is obtaining higher efficiency and power factor with lower voltage regulation and required permanent magnet volume compared to the initial design. In addition, the calculation of the voltage total harmonic distortion (THD) is presented and the optimum skew angle for the optimum generator is computed to reduce the voltage THD.

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A hybrid model for online prediction of PM2.5 concentration: A case study

2019

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In this paper, we aim at developing a model to predict the daily average concentration of particulate matters with a diameter of less than 2.5 micrometers (PM 2.5 ). In the introduced model, we incorporate Weather Research and Forecasting (WRF) meteorological model, Monte Carlo simulation, wavelet transform, and multilayer perceptron (MLP) neural networks. In particular, the MLP and wavelet transformation are combined for prediction. In order to predict the model's input parameters, including wind speed, wind direction, temperature, rainfall, and temperature inversion, the WRF meteorological model is used. Finally, according to the available uncertainty in the input data and in order to achieve a more accurate prediction, the Monte Carlo simulation is utilized. In order to assess the effectiveness of the model in the real world, it has been conducted in an online mode for 35 days. Numerical results give an acceptable accuracy in terms of some widely used measures. In particular, taking into account the R measurements, it is equal to 0.831 over the set of test instances.

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Multi-objective optimization design and performance evaluation of slotted Halbach PMSM using Monte Carlo method

Cited by 3 publications

References 16 publications

Analytical Study, Design and Optimization of Radial-Flux PM Limited-Angle Torque Motors

Analytical Study, Design and Optimization of Radial-Flux PM Limited-Angle Torque Motors

Design and Optimization of a Large-Scale Permanent Magnet Synchronous Generator

A hybrid model for online prediction of PM2.5 concentration: A case study

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