Modeling and analysis of switched reluctance generator using finite element method

Ganji, Babak; Heidarian, Mahdi; Faiz, Jawad

doi:10.1016/j.asej.2014.08.007

Cited by 32 publications

(14 citation statements)

References 20 publications

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“…The parameters of the z‐source converter are assumed to be L 1 = L 2 = 100 μH, C 1 = 3 μF and C 2 = 800 μF. The input dc voltage is 93 V and the rated speed is 1500 rpm as considered in Reference 44. The MPC parameters are N p = N c = 20 for the SRG controller and N = 25 and N u = 10 for the converter controller.…”

Section: Simulation Resultsmentioning

confidence: 99%

Model predictive control of switched reluctance generator based onZ‐source converterfor wind power applications

Kiani

Ganji

Taher

2020

Int Trans Electr Energ Syst

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Summary This paper presents a novel control drive for switched reluctance generator (SRG) driven from the wind power generation system. The SRG is controlled with a model predictive control (MPC) approach to generate the desired voltage applied to the phase winding being fed by a z‐source converter, for the first time. The converter itself is controlled with another MPC system to track the reference voltage of the previous section as fast as possible. To consider the nonlinear characteristics of the SRG, the flux linkage and electromagnetic torque have been derived from the finite element analysis of the machine and applied to the model. This novel drive maximizes the output power of the generator from three aspects. First, decreasing the rise‐up time of the phase current and the second, keeping the current in flat‐top mode at any speed and the third, maximum usage of the negative slope area of the phase inductance which is the generating zone. The whole of the system is simulated in MATLAB‐SIMULINK and the effectiveness of the proposed method is evaluated using the simulation results.

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Section: Simulation Resultsmentioning

confidence: 99%

Model predictive control of switched reluctance generator based onZ‐source converterfor wind power applications

Kiani

Ganji

Taher

2020

Int Trans Electr Energ Syst

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“…The distance X between the stream and the river is randomly updated using (18). where C is a value between [1,2], and d is the current distance.…”

Section: Startmentioning

confidence: 99%

“…Linear models in Refs [6,7] are readily constructed and simulated. In contrast, a large number of experimental tests are required to construct nonlinear models [8][9][10][11][12][13][14][15], or using a finite element method (FEM) analysis [16][17][18]. Some configurations of converters used in SRM drives are presented in Ref.…”

Section: Introductionmentioning

confidence: 99%

Optimal dynamic and steady‐state performance of switched reluctance motor using water cycle algorithm

El-Hay

Elkholy

2018

IEEJ Transactions Elec Engng

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Saliency and nonlinear magnetization characteristics of switched reluctance motor (SRM) are causes for its poor performance regarding torque ripples. This paper presents an innovative methodology based on a water cycle algorithm (WCA) for SRM speed control with minimum torque ripples and maximum torque per ampere. WCA enhances the dynamic and steady-state performance of this motor by optimizing a multiobjective function consisting of three parts. The first part is the integral time absolute error (ITAE) of motor speed with respect to a desired reference speed, the second part is an index for a motor torque smoothness factor (TSF), and the third part is the torque per ampere ratio (TAR). The desired output of WCA is the appropriate gains of the PI speed controller and optimum turn on and off angles of the H-bridge driving circuit. The results obtained from WCA are used to construct an adaptive controller-based artificial neural network (ANN) to automatically tune the gains of the PI speed controller and the turn on and off angles. The ANN controller ensures superior steady-state and dynamic motoring operation over a wide range of speed commands and different loads. The results of WCA are verified by other well-known meta-heuristic optimized techniques, such as the Genetic algorithm (GA). The suggested method exhibits an excellent speed transient response in addition to minimum torque ripples and maximum TAR.

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“…Finite element method [10] is now relatively mature numerical calculation technique, which can solve the complicated computational problems in engineering. The Maxwell 2D finite element model is created, and then defines the material properties.…”

Section: The Finite Element Model and Analysesmentioning

confidence: 99%

Finite element analysis and performance study of switched reluctance generator

Guo¹,

Xu²,

Yu³

et al. 2017

AIP Conference Proceedings

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Abstract. Analyses a three-phase 12/8 switched reluctance generator (SRG) which is based on its structure and performance principle. The initial size data were calculated by MathCAD, and the simulation model was set up in the ANSOFT software environment with the maximum efficiency and the maximum output power as the main reference parameters. The outer diameter of the stator and the inner diameter of the rotor were parameterized. The static magnetic field distribution, magnetic flux, magnetic energy, torque, inductance characteristics, back electromotive force and phase current waveform of SRG is obtained by analyzing the static magnetic field and the steady state motion of two-dimensional transient magnetic field in ANSOFT environment. Finally, the experimental data of the prototype are compared with the simulation results, which provide a reliable basis for the design and research of SRG wind turbine system.

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Modeling and analysis of switched reluctance generator using finite element method

Cited by 32 publications

References 20 publications

Model predictive control of switched reluctance generator based onZ‐source converterfor wind power applications

Model predictive control of switched reluctance generator based onZ‐source converterfor wind power applications

Optimal dynamic and steady‐state performance of switched reluctance motor using water cycle algorithm

Finite element analysis and performance study of switched reluctance generator

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