A New V &amp;#x00D7; I based adaptive speed sensorless four quadrant vector controlled induction motor drive

Chakraborty, Chandan; Teja, A. V. Ravi; Maiti, Suman; Hori, Yoichi

doi:10.1109/ipec.2010.5543736

Cited by 9 publications

(6 citation statements)

References 10 publications

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“…To carry out the stability analysis of any system, the variables must be time-invariant [15]. The IM model in synchronously rotating (ω e ) reference frame can be expressed as [13,14] p…”

Section: System Stability and Sensitivity Analysismentioning

confidence: 99%

“…The instantaneous value of X

false(normali . e . \overset{false¯}{v} * \times \overset{false¯}{i} false)

is obtained as [13, 14]

X_{1} = X_{ref} = v_{q s} i_{d s} + v_{d s} i_{q s}

The expression X 1 is independent of rotor speed and hence it is used as reference model.…”

Section: Model Formulation Algorithmmentioning

confidence: 99%

“…This also makes the estimation at or near zero speed quite accurate [6]. The instantaneous and steady‐state values of

bold-italicv * \times \overset{false¯}{i}

[7, 13] are considered to develop the reference and adaptive models respectively in newly reported X ‐MRAS [14] where, X is a fictitious quantity i.e., neither active power nor reactive power. The X ‐MRAS is also reported to be stable in all the 4‐quardants of operation.…”

Section: Introductionmentioning

confidence: 99%

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Comparative assessment of two different model reference adaptive system schemes for speed‐sensorless control of induction motor drives

Kumar

Das

Chattopadhyay

2016

IET Electric Power Applications

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This study presents a comparison between a well-known instantaneous reactive power (Q)-based model reference adaptive system (MRAS) (Q-MRAS) and a relatively new X-MRAS used for the speed estimation of induction motor drive without speed sensor from the stability point of view. Stability aspects and sensitivity analysis of both the schemes to machine parameters variation are examined. The instantaneous and steady-state values of Q are considered to develop the reference and adaptive models respectively for the former, whereas those of a fictitious quantity X (i.e. n*× i) are considered in the said models for the later. Non-computation of flux in these schemes leads to the satisfactory estimation of speed over a wide range maintaining the drive's stability in all the 4-quadrants. Both the Q-a n dX-MRAS are found to be dependent on the rotor resistance variation, whereas X-MRAS is also influenced by the stator resistance variation. A small signal stability study over a wide range of stator and rotor resistance variations is carried out to investigate the drive's stability in the low-speed region for both the MRAS schemes. Matlab/ Simulink is used to obtain the simulation results for the proposed study. Experimental results are obtained through a dSPACE-1104-based laboratory prototype. Nomenclature v ds , v qs d-and Q-axis components of stator voltage (V) i ds , i qs d-andQ-axis components of stator current (A) *, superscript s corresponds to the reference and stationary quantity i ds0 , i qs0 d-and Q-axis nominal stator current (A) c dr , c qr d-andQ-axis components of rotor flux (Wb) ω e synchronous speed of the rotor (rad/s) v r ,v r rotor and estimated speed (rad/s) v sl ,v sl slip and estimated slip speed (rad/s

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“…To carry out the stability analysis of any system, the variables must be time-invariant [15]. The IM model in synchronously rotating (ω e ) reference frame can be expressed as [13,14] p…”

Section: System Stability and Sensitivity Analysismentioning

confidence: 99%

“…The instantaneous value of X

false(normali . e . \overset{false¯}{v} * \times \overset{false¯}{i} false)

is obtained as [13, 14]

X_{1} = X_{ref} = v_{q s} i_{d s} + v_{d s} i_{q s}

The expression X 1 is independent of rotor speed and hence it is used as reference model.…”

Section: Model Formulation Algorithmmentioning

confidence: 99%

“…This also makes the estimation at or near zero speed quite accurate [6]. The instantaneous and steady‐state values of

bold-italicv * \times \overset{false¯}{i}

Section: Introductionmentioning

confidence: 99%

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Comparative assessment of two different model reference adaptive system schemes for speed‐sensorless control of induction motor drives

Kumar

Das

Chattopadhyay

2016

IET Electric Power Applications

View full text Add to dashboard Cite

show abstract

“…A novel MRAS (introduced as X-MRAS)-based speed estimator designed with instantaneous and steady-state values of V*×I (or v*×i) (where V = stator voltage vector and I = stator current vector) in the reference and adaptive models, respectively, is presented in [54][55][56][57]. In this X-MRAS ( Fig.…”

Section: New X-mrasmentioning

confidence: 99%

“…On the contrary, the adaptive model is used to observe the same state variables with different sets of equations employing different inputs which include the parameter to be estimated. The stability of a drive system is achieved by minimising the error between the signals based on either rotor flux [4, 11, 47] or back EMF [37, 48] or reactive power [6, 7, 49–52] or active power or electromagnetic torque [50, 53] or V * × I [54–57] obtained from the two models. A set of adaptive laws such as Popov's criterion of hyperstability [58], Lyapunov stability theorem [59, 60] and recursive least‐square (RLS) algorithm [61] has been widely considered to minimise the error signal.…”

Section: Model Reference Adaptive Systemmentioning

confidence: 99%

Review on model reference adaptive system for sensorless vector control of induction motor drives

Kumar

Das

Syam

et al. 2015

IET Electric Power Applications

182

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The most frequently used electrical machine in various modern high-performance drive applications is the induction motor (IM), especially its squirrel cage type rotor counterpart. The high-precision, efficient control of sensorless IM drives throughout its operating range demands an exact knowledge of a few of the IM parameters. The stator resistance, rotor resistances are such important IM parameters whose variations may lead to improper estimation of speed over the whole operating range in sensorless drives. Numerous methods for the estimation of speed of high-performance sensorless IM drives taking into account the effect of parameter variations have been developed. This paper aims at providing a review of the major model reference adaptive system (MRAS) based techniques applied for the estimation of speed of such sensorless drives. This paper is illustrated throughout with relevant mathematical equations, experimental and simulation results, associated to different MRAS-based sensorless vector control of the IM drive.Nomenclature v s stator voltage vector i s stator current vector i m = i s + (L r /L m )i r ,î m magnetising and estimated current vector e m back EMF vector (V) v ds , v qs d-axis and q-axis components of stator voltage (V) i ds , i qs d-axis and q-axis components of stator current (A) c dr , c qr d-axis and q-axis components of rotor flux (Wb) c dr ,ĉ qr estimated d-axis and q-axis components of rotor flux (Wb) c r ,ĉ r rotor flux (Wb), estimated rotor flux (Wb) K p , K i proportional, integral gain constants ε r , ε rotor-flux-error signal (Wb), error signal R r R s rotor, stator resistance referred to stator (Ω) L r , L s , L m rotor self-, stator self-, mutual inductance referred to stator (H) X r , X s , X m rotor self-, stator self-, mutual reactance referred to stator (Ω) L ′ m = L 2 m /L r equivalent mutual inductance (H) T r = L r /R r rotor time constant (s) s = 1 − L 2 m /(L s L r ) total machine leakage factor v r , ω r , v * r estimated, actual and reference rotor speed (rad/s) Δe s error in estimated back EMF (V) p derivative operator k ωr speed gain q m ,q m instantaneous and estimated reactive power volt-ampere reactive (VAR) ω e , Δω e speed and speed-deviation of rotating reference frame (rad/s) e ds, qs = i ds, qs −î ds, qs error between measured and estimated stator currents (A) e c control signal based on combined stator current error and rotor flux

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Applications and Specifics

2021

Electrical Safety Engineering of Renewable EnergySystems

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A New V × I based adaptive speed sensorless four quadrant vector controlled induction motor drive

Cited by 9 publications

References 10 publications

Comparative assessment of two different model reference adaptive system schemes for speed‐sensorless control of induction motor drives

Comparative assessment of two different model reference adaptive system schemes for speed‐sensorless control of induction motor drives

Review on model reference adaptive system for sensorless vector control of induction motor drives

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