Modeling of a Tree-Phase 12/8 Pole Switched Reluctance Motor in MATLAB

Yankov, Dimitar; Grigorova, Tsvetana; Dinkov, Emil

doi:10.1109/et.2019.8878654

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…The tested SRM is fed from the asymmetric bridge converter. This topology is the most widely used in SRM drives in terms of converter losses and cost, control performance, the independent phase windings, Int J Pow Elec & Dri Syst ISSN: 2088-8694  allowing a fault-tolerant operation capability [7]. Hence, for example, the terminals named "A+" and "A-" (block phase A) connect the motor phase A to the corresponding bridge of the asymmetric converter circuit.…”

Section: The Implementation Of the Srm Nonlinear Model In Simulinkmentioning

confidence: 99%

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A nonlinear model for a three-phase 12/8 switched reluctance machine

Yankov

Grigorova

2022

IJPEDS

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The paper presents a methodology for the synthesis of a nonlinear model for a three-phase 12/8 switched reluctance machine (SRM) in a MATLAB/Simulink environment. The required switched reluctance motor characteristics are derived using the finite element analysis (FEA). Graphical and tabular results of the relations flux/current/position, current/torque/position, and inductance/current/position used to create the SRM model are presented. The structure of the nonlinear three-phase 12/8 SRM model is discussed and its implementation in Simulink is presented. A series of simulation and experimental results are performed to verify the accuracy of the built three-phase, 12/8 SRM (H55PWBKM-1844) nonlinear model. A very good alignment between simulation and experimental results is observed.

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Section: The Implementation Of the Srm Nonlinear Model In Simulinkmentioning

confidence: 99%

“…Furthermore, the phase inductances change linearly from the rotor position. Linear models of three-phase 6/4 SRM and three-phase 12/8 SRM are demonstrated in [5]- [7]. These models combine the ability to obtain acceptable results with minimal simulation time.…”

Section: Introductionmentioning

confidence: 99%

A nonlinear model for a three-phase 12/8 switched reluctance machine

Yankov

Grigorova

2022

IJPEDS

Self Cite

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Adaptive Weighted Particle Swarm Optimization for Controlling Multiple Switched Reluctance Motors with Enhanced Deviatoric Coupling Control

Zhang,

Xu,

Zhang

et al. 2024

Electronics

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Switched reluctance motors (SRMs) are widely used in industrial applications due to their advantages. Multi-motor synchronous control systems are crucial in modern industry, as their control strategies significantly impact synchronization performance. Traditional deviation coupling control structures face limitations during the startup phase, leading to excessive tracking errors and exacerbated by uneven load distribution, resulting in desynchronized motor acceleration and increased speed synchronization errors. This study proposes a modified deviation coupling control method based on an adaptive weighted particle swarm optimization (PSO) algorithm to enhance multi-motor synchronization performance. Traditional deviation coupling control applies equal reference torque inputs to each motor’s current loop, failing to address uneven load distribution and causing inconsistent accelerations. To resolve this, a gain equation based on speed deviation is introduced, incorporating self-tracking error and gain coefficients for dynamic synchronization error compensation. The gain coefficients are optimized using the adaptive weighted PSO algorithm to improve system adaptability. A simulation model of a synchronization control system for three SRMs was developed in the Matlab/Simulink R2023b environment. This model compares the synchronization performance of traditional deviation coupling, Fuzzy-PID improved structure, and adaptive PSO improved structure during motor startup, sudden speed increases, and load disturbances. The validated deviation coupling control structure achieved the initial set speed in approximately 0.236 s, demonstrating faster convergence and a 6.35% reduction in settling time. In both the motor startup and sudden speed increase phases, the two optimized methods outperformed the traditional structure in dynamic performance and synchronization accuracy, with the adaptive PSO structure improving synchronization accuracy by 54% and 37.17% over the Fuzzy-PID structure, respectively. Therefore, the PSO-optimized control system demonstrates faster convergence, improved stability, and enhanced synchronization performance.

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Modeling of a Tree-Phase 12/8 Pole Switched Reluctance Motor in MATLAB

Cited by 2 publications

References 4 publications

A nonlinear model for a three-phase 12/8 switched reluctance machine

A nonlinear model for a three-phase 12/8 switched reluctance machine

Adaptive Weighted Particle Swarm Optimization for Controlling Multiple Switched Reluctance Motors with Enhanced Deviatoric Coupling Control

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