A new EKF-based algorithm for flux estimation in induction machines

Salvatore, L.; Stasi, S.; Tarchioni, L.

doi:10.1109/41.238018

Cited by 105 publications

(38 citation statements)

References 6 publications

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“…Choosing the stator current and the rotor flux as the state variables and the stator voltage as input variables, the state equation of an induction motor with the d − q coordinates fixed in the stator (stator reference frame (α, β)) can be expressed as [14] i s φ r = ρ 11 ρ 12 ρ 21 ρ 22…”

Section: Numerical Simulationmentioning

confidence: 99%

Synchronization of Two Non-Identical Coupled Exciters in a Non-Resonant Vibrating System of Linear Motion. Part II: Numeric Analysis

Zhao

Zhu

Bai

et al. 2009

Shock and Vibration

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Abstract. The paper focuses on the quantitative analysis of the coupling dynamic characteristics of two non-identical exciters in a non-resonant vibrating system. The load torque of each motor consists of three items, including the torque of sine effect of phase angles, that of coupling sine effect and that of coupling cosine effect. The torque of frequency capture results from the torque of coupling cosine effect, which is equal to the product of the coupling kinetic energy, the coefficient of coupling cosine effect, and the sine of phase difference of two exciters. The motions of the system excited by two exciters in the same direction make phase difference close to π and that in opposite directions makes phase difference close to 0. Numerical results show that synchronous operation is stable when the dimensionless relative moments of inertia of two exciters are greater than zero and four times of their product is greater than the square of their coefficient of coupling cosine effect. The stability of the synchronous operation is only dependent on the structural parameters of the system, such as the mass ratios of two exciters to the vibrating system, and the ratio of the distance between an exciter and the centroid of the system to the equivalent radius of the system about its centroid.

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Section: Numerical Simulationmentioning

confidence: 99%

Synchronization of Two Non-Identical Coupled Exciters in a Non-Resonant Vibrating System of Linear Motion. Part II: Numeric Analysis

Zhao

Zhu

Bai

et al. 2009

Shock and Vibration

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“…Example 3 presents estimation of the states of a nonlinear symmetrical three phase induction machine, more specifically, the flux and angular velocity estimation ( [13][14][15]). The state space representation of the induction machine is as follows:…”

Section: Example 3 Symmetrical Three Phase Induction Machinementioning

confidence: 99%

Family of state space least mean power of two-based algorithms

Moinuddin¹,

Al‐Saggaf²,

Ahmed³

2015

EURASIP J. Adv. Signal Process.

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In this work, a novel family of state space adaptive algorithms is introduced. The proposed family of algorithms is derived based on stochastic gradient approach with a generalized least mean cost functionfor any integer L. Since this generalized cost function is having power '2L', it includes the whole family of the power of two-based algorithms by having different values of L. The novelty of the work resides in the fact that such a cost function has never been used in the framework of state space model. It is a well-known fact that the knowledge of state space model improves the estimation of state parameters of that system. Hence, by employing the state space model with a generalized cost function, we provide an efficient way to estimate the state parameters. The proposed family of algorithms inherit simplicity in its structure due to the use of stochastic gradient approach in contrast to the other model-based algorithms such as Kalman filter and its variants. This fact is supported by providing a comparison of the computational complexities of these algorithms. More specifically, the proposed family of algorithms has computational complexity far lesser than that of the Kalman filter. The stability of the proposed family of algorithms is analysed by providing the convergence analysis. Extensive simulations are presented to provide concrete justification and to compare the performances of the proposed family of algorithms with that of the Kalman filter.

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“…Nowadays, a number of estimation techniques are available for speed and flux calculation. The standard speed estimators are Extended Kalman Filter (EKF) [1]- [6], Luenberger observer [7]- [9] and Model Reference Adaptive System (MRAS) [10]. The initial selection of noise covariance matrices is not easy in EKF and subsequently the algorithm is complicated.…”

Section: Introductionmentioning

confidence: 99%

Speed Sensorless Vector Control of Induction Motor Drive with PI and Fuzzy Controller

Gunabalan¹,

Subbiah

2015

IJPEDS

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This paper directed the speed-sensorless vector control of induction motor drive with PI and fuzzy controllers. Natural observer with fourth order state space model is employed to estimate the speed and rotor fluxes of the induction motor. The formation of the natural observer is similar to and as well as its attribute is identical to the induction motor. Load torque adaptation is provided to estimate the torque and rotor speed is estimated from the load torque, rotor fluxes and stator currents. There is no direct feedback in natural observer and also observer gain matrix is absent. Both the induction motor and the observer are characterized by state space model. Simple fuzzy logic controller and conventional PI controllers are used to control the speed of the induction motor in closed loop. MATLAB simulations are made with PI and fuzzy controllers and the performance of fuzzy controller is better than PI controller in view of torque ripples. The simulation results are obtained for various running conditions to exhibit the suitability of this method for sensorless vector control. Experimental results are provided for natual observer based sensorless vector control with conventional PI controller. Keyword:Fuzzy INTRODUCTIONInduction motors are preferred for most of the industry applications because of the limitations of commutation and rotor speed in DC drives. The induction motor is in fact 'brushless' and can operate with simple control methods not requiring a shaft position transducer. With no shaft position feedback, the motor remains stable only as long as the load torque does not exceed the breakdown torque. At low speeds it is possible for oscillatory instabilities to develop. To overcome these limitations 'field-oriented' or 'vector' control has been developed in which the phase and magnitude of the stator currents are regulated so as to maintain the optimum angle between stator mmf and rotor flux. This control is based on transforming a three phase time and frequency dependent system into a two co-ordinate (d and q axes) time invariant system. These projections lead to a structure similar to that of a separately excited DC motor control. Field orientation, however, requires either a shaft position encoder or an in-built control model whose parameters are specific to the motor.Generally, two types of field oriented control schemes are available. 1. Direct field oriented control 2. Indirect field oriented control. In the direct scheme, the instantaneous position of rotor flux (θ e ) has to be measured using flux sensors. This adds to the cost and complexity of the drive system. In the indirect scheme, a model of the induction motor is required to calculate the reference angular slip frequency that has to be added to the measured rotor speed. The sum is integrated to calculate the instantaneous position of the

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A new EKF-based algorithm for flux estimation in induction machines

Cited by 105 publications

References 6 publications

Synchronization of Two Non-Identical Coupled Exciters in a Non-Resonant Vibrating System of Linear Motion. Part II: Numeric Analysis

Synchronization of Two Non-Identical Coupled Exciters in a Non-Resonant Vibrating System of Linear Motion. Part II: Numeric Analysis

Family of state space least mean power of two-based algorithms

Speed Sensorless Vector Control of Induction Motor Drive with PI and Fuzzy Controller

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