Improved dq-Axes Model of PMSM Considering Airgap Flux Harmonics and Saturation

Fasil, Muhammed; Antaloae, Ciprian; Mijatović, Nenad; Jensen, Bogi Bech; Holbøll, Joachim

doi:10.1109/tasc.2016.2524021

Cited by 29 publications

(11 citation statements)

References 12 publications

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“…The first and foremost job is to find a linear model of the permanent magnet synchronous motor from the differential equations of the plant [16][17][18]. The differential equations for the PMSM in d-q reference frame is given by the following equations,…”

Section: State Model Of Pmsmmentioning

confidence: 99%

Speed control of permanent magnet synchronous motor using neural network model predictive control

Sundharajan

Premkumar²,

Vishnupriya

2020

Journal of Energy Systems

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Model predictive control has been widely used in the industry. This can control the multivariable system with constraints on input and output variables but it needs online computation solver, and creates the nonconvex solution in nonlinear plant due to the parameter uncertainties. The online computational problem and non-convex solution of the model predictive control are achieved via neural network model predictive control. The paper explores the speed control of permanent magnet synchronous motor (PMSM) by using neural network model predictive control (NNMPC) technique. The multi-layer artificial neural network is used to identify the dynamics of PMSM. The set point speed tracking control of PMSM is identified by using neural network model predictive control strategy. By using the set of input and output data obtained from the system, the multi input-output feed-forward neural network model is created. Levenberg-Marquardt algorithm is used to train the process models of the PMSM. That provides future plant output for control optimization of the predictive control. The overall system is developed and tested in the MATLAB/Simulink. To evaluate the efficiency of the controller proposed, it is compared with a constrained model predictive controller through the studies of simulation. The overshoot and settling time of the speed response of the PMSM are measured and analyzed for NNMPC and constrained MPC.

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Section: State Model Of Pmsmmentioning

confidence: 99%

Speed control of permanent magnet synchronous motor using neural network model predictive control

Sundharajan

Premkumar²,

Vishnupriya

2020

Journal of Energy Systems

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“…As shown in Fig. 6b, the sampling period of symmetric PWM is T s and the finetuned interrupt time is T sub which is calculated by software as shown in (18) and stored in the internal timer of DSP at every sampling time instant. Whenever the timer times out, the interruption occurs to initiate the external hardware circuit.…”

Section: Proposed Torque Control Under Six-step Voltage Operationmentioning

confidence: 99%

“…The research in [16] has nearly the same conclusion as [15]. To analyse the influence of core saturation, armature reaction, and cross-coupling between d-q axes, finite element analysis (FEA) is applied to the IPMSM [17][18][19][20] to get the key equations. Jeong and Nam [17] attempted to develop a closed form of torque and inductances based on flux linkages and the accuracy is evaluated by FEA and experimental results.…”

Section: Introductionmentioning

confidence: 99%

Torque control in constant power region for IPMSM under six‐step voltage operation

Huang

Chen

et al. 2018

IET Electric Power Applications

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To increase output torque in the constant power region of interior permanent magnet synchronous motor (IPMSM) for electric vehicle/hybrid electric vehicle applications, a novel torque control based on voltage phase angle control is proposed to provide more accurate torque for the IPMSM. First, the mathematic model of the IPMSM under six-step voltage operation is derived. Then, the phasor diagram based on the fundamental frequency and ideal condition is used to explain the relation between the torque, power, and voltage phase angle. Moreover, Maxwell 2D simulation for the IPMSM shows the detailed phenomena including generated torque, phase current, and magnetic flux distribution for developing controllers. Hence, the proposed method uses phase voltage advancing angle to yield torque, which consists of a command feed-forward controller to give a fast dynamic response, a proportional-integral regulator, a high-resolution voltage angle injection to reduce torque ripple and an efficiency-based torque estimator to enhance torque accuracy under six-step voltage operation. Finally, a digital signal processor(DSP)-based motor drive is built to verify the proposed method using a 6 kW/47 Nm/1200 rpm IPMSM. The experimental results measured by a torque transducer are derived to show that the torque ripple is significantly reduced, torque accuracy is improved, and constant power control achieved is from 1800 to 3600 rpm. These results, therefore, confirm the superior performance of the proposed control method.

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“…Due to these advantages, several recent works present models that are based on dq0 signals. A few examples are synchronous machines [13][14][15], renewable energy sources [16][17][18][19], microgrids [20][21][22] and complete networks [20,23,24]. See [25] for a more detailed survey.…”

Section: Introductionmentioning

confidence: 99%

A Tutorial on Dynamics and Control of Power Systems with Distributed and Renewable Energy Sources Based on the DQ0 Transformation

2018

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In light of increasing integration of renewable and distributed energy sources, power systems are undergoing significant changes. Due to the fast dynamics of such sources, the system is in many cases not quasi-static, and cannot be accurately described by time-varying phasors. In such systems the classic power flow equations do not apply, and alternative models should be used instead. In this light, this paper offers a tutorial on the dynamics and control of power systems with distributed and renewable energy sources, based mainly on the dq0 transformation. The paper opens by recalling basic concepts of dq0 quantities and dq0-based models. We then explain how to model and analyze passive networks, synchronous machines, three-phase inverters, and how to systematically construct dq0-based models of complex systems. We also highlight the idea that dq0 models may be viewed as a natural extension of time-varying phasor models, and discuss the correct use and validity of each approach.

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Improved dq-Axes Model of PMSM Considering Airgap Flux Harmonics and Saturation

Cited by 29 publications

References 12 publications

Speed control of permanent magnet synchronous motor using neural network model predictive control

Speed control of permanent magnet synchronous motor using neural network model predictive control

Torque control in constant power region for IPMSM under six‐step voltage operation

A Tutorial on Dynamics and Control of Power Systems with Distributed and Renewable Energy Sources Based on the DQ0 Transformation

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