Online Identification of PMSM Parameters: Parameter Identifiability and Estimator Comparative Study

Boileau, Thierry; Leboeuf, N.; Nahid‐Mobarakeh, Babak; Meibody‐Tabar, Farid

doi:10.1109/tia.2011.2155010

Cited by 183 publications

(83 citation statements)

References 19 publications

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“…Also, more advanced techniques of inertia identification using observer (Awaya et al, 1992;Lee et al, 2004;Yang and Deng, 2005), extended Kalman filtering (Kwon et al, 2006;Boileau et al, 2008;Hong et al, 1996), model reference adaptive method (Andoh, 2008;Okamura et al, 1995), gradient descent method (Boileau et al, 2011) are available in literature. These methods, generally needs a more complicated hardware.…”

Section: Fig 2: Typical Deceleration Curve For Retardation Testmentioning

confidence: 99%

A simple method for electrical machine’s mechanical parameter extraction

Abut

Kandilli

2017

Int. j. adv. appl. sci

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Electrical motors are one of the most important key components of industry. While motors can be divided in so many groups, mathematical description of all of them is divided into two subsystems: Electrical and mechanical subsystem. Mechanical subsystem is usually composed of rotor inertia, external load and friction in bearing of rotor. Friction is usually modeled as viscous friction, i.e. linearly dependent on angular speed. Rotor inertia J) and coefficient of viscous friction (B) are needed in order to model mechanical subsystem of motor. When motor is used in high performance close loop motion control systems, an accurate model of motor is required for system analysis and design. This paper suggests a novel method for measuring rotor's J and B for such applications. There is no restriction on the type of motor under test. Studied method, needs no sensor so no friction is added to motor. Only a digital camera is required. There is no need to open the motor case and remove motor's rotor in this method. Proposed method has been tested in laboratory and practical results shows effectiveness of suggested method.

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Section: Fig 2: Typical Deceleration Curve For Retardation Testmentioning

confidence: 99%

A simple method for electrical machine’s mechanical parameter extraction

Abut

Kandilli

2017

Int. j. adv. appl. sci

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“…In this section, the latter application for an IPMSM will be given a special attention. The proposed EKF provides estimated d-axis and q-axis inductance [7,8].…”

Section: Extended Kalman Filter For Parameter Estimationmentioning

confidence: 99%

“…In this paper, the initial values of covariance matrix P 0 and system matrix Q and R have been chosen with a trialand-error procedure to get the best tradeoff between filter stability and convergence time [8]. As the value of Q increases, Kalman gain also increases, which causes variation of estimation values.…”

Section: Extended Kalman Filtermentioning

confidence: 99%

“…Several on-line parameter estimations such as the extended Kalman filter (EKF), recursive least square (RLS), and model reference adaptive system (MRAS) have been proposed [5][6][7][8][9][10]. Among them, the extended Kalman filter is the most effective estimator in terms of the leastsquare for estimating the states of nonlinear systems, which is very appropriate for implementation in systems with sensors affected by noise.…”

Section: Introductionmentioning

confidence: 99%

“…This is feasible because a mathematical model, which describing the motor dynamics is very well known. The terminal quantities like voltages and currents can be measured easily and are appropriate for the determination of the parameters of the machine in an indirect way [7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On-line Parameter Estimation of Interior Permanent Magnet Synchronous Motor using an Extended Kalman Filter

Sim¹,

Lee²,

Lee³

2014

Journal of Electrical Engineering and Technology

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-This paper presents estimation of d-axis and q-axis inductance of an interior permanent magnet synchronous motor (IPMSM) by using an extended Kalman filter (EKF). The EKF is widely used for control applications including the motor sensorless control and parameter estimation. The motor parameters can be changed by temperature and air-gap flux. In particular, the variation of the inductance affects torque characteristics like the maximum torque per ampere (MTPA) control. Therefore, by estimating the parameters, it is possible to improve the torque characteristics of the motor. The performance of the proposed estimator is verified by simulations and experimental results based on an 11kW PMSM drive system.

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Sensorless vector control of permanent magnetic synchronous motor with parameter error compensation

Pak

Kang

2021

Int Trans Electr Energ Syst

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The position sensorless vector control of permanent magnetic synchronous motor is the method that implements the vector control by estimating the rotor position from the motor model in the basis of the voltage and the current signal of the motor without measuring the motor position directly. The sensorless vector control is divided into two methods: estimating the rotor position based on the stationary reference frame and rotational reference frame. This article presents the research results of more improved estimation performance by combining these two methods. First, we estimate the rotor position by using the open-loop estimation algorithm based on the stationary reference frame, evaluate the estimation error based on the model from the rotational reference frame and compensate the estimate in a feedback way with differences from the current measurements. At the same time, we also take the parameter errors included in the model into account. This article performed the experiment for the parameter identification and for the sensorless vector control, and demonstrated its effectiveness.

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Online Identification of PMSM Parameters: Parameter Identifiability and Estimator Comparative Study

Cited by 183 publications

References 19 publications

A simple method for electrical machine’s mechanical parameter extraction

A simple method for electrical machine’s mechanical parameter extraction

On-line Parameter Estimation of Interior Permanent Magnet Synchronous Motor using an Extended Kalman Filter

Sensorless vector control of permanent magnetic synchronous motor with parameter error compensation

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