A new approach in sensor-less vector control of stand-alone doubly fed induction generator

Pourjafari, Mojtaba; Choolabi, Esmael Fallah

doi:10.1007/s00202-023-01760-3

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…The simulation findings show the correlation between this construction, optimal permanent magnet, and optimal segmentation design of permanent magnet on the torque ripple [10]. Pourjafari found that both cogging torque and torque ripple can be reduced using significant elements such as magnet embrace, offset, and skew [11]. Srisiriwanna investigated the cogging torque reductions of a BLDC motor with varying air gap length, rotor, and stator parts.…”

Section: Introductionmentioning

confidence: 91%

Effect of Changing Magnet Material on Cogging Torque and Torque Ripple of Brushless DC Motor

2023

IJSER

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Brushless DC motors (BLDC)are extensively used for high-control applications because of their small size, high efficiency, and high-performance qualities. It gained popularity since the permanent magnetic materials were improved, so the drive system must have precise positioning and smooth electromagnetic torque for high performance and precision. Numerous elements contribute to torque ripples in this motor; one of them is the type of permanent magnet material. In this paper, a 3-phase, four-pole, 1500 W BLDC motor was modeled based on RMxprt and Maxwell 2D software. Four magnet materials: alnico 5, ceramic 5, SmCo 28, and NdFe 35, were studied to show the effect of each magnet on motor performance. The finite element analysis (FEA) results showed that the motor's average torque and cogging torque are directly proportional to the magnet strength. Also, the torque ripple exists with using a lower magnet strength magnet. Ndfe35 and SmCo28 have strong mechanical properties, are relatively expensive, and have higher cogging torque and torque ripple. The other materials, Alnico 5 and ceramic 5, are less costly and have less cogging torque and torque ripple. The full load simulations demonstrate that weaker magnets can also provide lower torque. Due to the results presented, while choosing the magnet materials, the trade-off between nominal torque, torque ripple, and price should be taken into account. The methodology adopted in this research will allow motor designers to select the best magnet material,and leading to excellent motor performance without the requirement to construct and test several prototype motors.

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Section: Introductionmentioning

confidence: 91%

Effect of Changing Magnet Material on Cogging Torque and Torque Ripple of Brushless DC Motor

2023

IJSER

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Particle Swarm Optimization Algorithm and its Application in Parameter Identification of Generator Set Speed Regulation System

Yu,

Hu,

Liu

2023

2023 3rd Asian Conference on Innovation in Technology (ASIANCON)

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PSO-optimized sensor-less sliding mode control for variable speed wind turbine chains based on DPIG with neural-MRAS observer

Saihi,

Ferroudji,

Roummani

et al. 2024

Wind Engineering

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This research introduces a resilient Sensor-Less 1st Sliding Mode (SL-FOSM) approach employing a novel observer, the Artificial Neural Network with Model Reference Adaptive System-Adaptive (Neural-MRAS), for wind turbine chains. The proposed model is implemented on a Doubly Powered Induction Generator (DPIG) operating under genuine variable speed conditions in the Adrar region in Algeria. The control objective is to independently regulate the active and reactive power of the DPIG stator, achieved through decoupling using the field-oriented control technique and control application via FOSM-C. Notably, this methodology reduces both the control scheme cost and the DPIG size by eliminating the need for a speed sensor (encoder). To enhance the MRAS-PI, an Artificial Neural Network (ANN) is suggested to replace the typical classical Proportional-Integral (PI) controller in the adaptation mechanism of MRAS. The rotor position estimation is scrutinized and discussed across various load conditions in low, zero, and high-speed regions. Optimal controller parameters are determined through particle swarm optimization (PSO). The results demonstrate that the proposed observer (Neural-MRAS) exhibits compelling attributes, including guaranteed finite time convergence, robust performance in response to speed variations, high resilience against machine parameter fluctuations, and adaptability to load variations when compared to the MRAS-PI. Consequently, the estimated rotor speed converges to its actual value, showcasing the capability to accurately estimate position across different speed regions (low/zero/high).

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A new approach in sensor-less vector control of stand-alone doubly fed induction generator

Cited by 3 publications

References 24 publications

Effect of Changing Magnet Material on Cogging Torque and Torque Ripple of Brushless DC Motor

Effect of Changing Magnet Material on Cogging Torque and Torque Ripple of Brushless DC Motor

Particle Swarm Optimization Algorithm and its Application in Parameter Identification of Generator Set Speed Regulation System

PSO-optimized sensor-less sliding mode control for variable speed wind turbine chains based on DPIG with neural-MRAS observer

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