Improved performance of motor drive using RBFNN-based hybrid reactive power MRAS speed estimator

Xiao, Jinfeng; Li, Biwen; Xue-yu, Gong; Sheng, Yifa; Chai, Jun

doi:10.1109/icinfa.2010.5512404

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…Using (12) as the reference model and (7), (13) together as the adaptive model, a reactive power-error-based MRAS which is completely robust to stator and rotor resistance thermal variations are achieved (Fig. 6).…”

Section: Reactive Power-error-based Mrasmentioning

confidence: 99%

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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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Section: Reactive Power-error-based Mrasmentioning

confidence: 99%

“…A considerable research effort has been made on the sensorless control of IM drives [3][4][5][6][7][8][9][10][11][12][13][14][15]. The key principle behind all the sensorless means of controlling IM drives is the vector control or field-oriented control (FOC) strategy.…”

Section: Introductionmentioning

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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“…Then the response of the error can be represented by [65,67] dc sd dt ¼ ÀR [37][38][39][40][41][42][43][44][45][46][47][48][49]. The model reference approach makes use of two independent machine models of different structures to estimate the same state variable (back emf, rotor flux, reactive power, etc.)…”

Section: Adaptive Backstepping Observermentioning

confidence: 99%

Sensorless Speed Control of AC Machines

Abu‐Rub

Iqbal

Guziński

2012

High Performance Control of AC Drives With MATLAB/Simulink Models

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Sensorless speed control of induction machine (IM) drives has, in the past decade, become a mature technology for a wide speed range [1][2][3].The elimination of the rotor speed sensor, without affecting performance, is a major trend in advanced drives control systems [1]. The advantages of speed sensorless AC drives are reduced hardware complexity, lower costs, elimination of the sensor cable, better noise immunity, increased reliability, access to both sides of the shaft, less maintenance requirements, and higher robustness. An encoder is expensive and a problematic factor. The special motor shaft extension increases the drive's price. The use of encoders affects the reliability, particularly in hostile environments. In general, the operation in explosive, corrosive, or chemically aggressive environments requires a motor without a speed sensor.A variety of different solutions for sensorless AC drives have been proposed, mostly in the past two decades. Their advantages and limits are reviewed in many survey papers .Many methods are generally accepted as better solutions for high sensorless performance, for example, model reference adaptive system (MRAS), Kalman Filters, adaptive non-linear flux observer, sliding mode observers, and other improvements [7-14]. Sensorless Control of Induction MotorTwo basic approaches are used for sensorless control. The first includes methods that model the induction motor by its state equations [1]. A sinusoidal magnetic field in the air gap is assumed.High Performance Control of AC Drives with MATLAB/Simulink Models, First Edition. Haitham Abu-Rub, Atif Iqbal, and Jaroslaw Guzinski.The models are either implemented as open-loop structures, like the stator model [1,11], or as closed-loop models, like adaptive observers [5,17]. The adaptive flux observers are now receiving considerable attention and many achieving new solutions because of their high precision and relative robustness against machine parameter deviation [1,10].Open loop models and even adaptive observers have stability limits at very low stator frequencies. The rotor induced voltage at such operating points is then zero or close to zero, which renders the induction motor an unobservable system.The basic limitation for sensorless operation is the DC offset components in the stator current and voltage acquisition channels at very low speeds [1]. At lower speeds, voltage distortions caused by the PWM inverter become significant.The second approach used for low speed sensorless operation is the signal injection technique [1]. Carrier injection methods for sensorless control are sophisticated and the design must match the properties of the motor [1,4,18,41]. This makes the method unfeasible for practical application, so will not be discussed in this book.Eliminating the speed sensor from the drive system requires estimation of the state variables, for example, motor speed and machine flux using stator variables. In this chapter we will discuss a few simple solutions for sensorless AC motor drives.

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“…In addition, model reference adaptive system (MRAS)‐based control techniques have been overlooked yet for this machine. The MRAS‐based approaches are simple in formulation and implementation; and inherently take the machine parameter variations into account with minimum computational effort [18]. Since, MRAS‐based control techniques have been used satisfactorily in the other AC drives, these techniques are also resourceful for BDFRM drive as well.…”

Section: Introductionmentioning

confidence: 99%

Implementation of reactive power‐based MRAS for sensorless speed control of brushless doubly fed reluctance motor drive

Kiran

Das

2018

IET Power Electronics

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The present work introduces the concept of classical reactive power (Q)-based model reference adaptive system (MRAS) for the speed estimation and control of low-cost BDFR motor drive. The reasons behind such choice for this drive's control are: (i) MRAS-based controllers can inherently take care of the machine parameter variations which had been a challenging issue with the other available sensorless speed estimation strategies and (ii) the employment of Q as a functional candidate inevitably makes the formulation immune to the variations in stator resistance. The experimental assessment, in this respect, confirms these claims. Furthermore, the analytical validation of Popov's hyperstability criteria on the proposed controller, confirms the drive's overall stability within the investigated speed control range. The speed control performance is examined in MATLAB/Simulink. The simulation results are further validated by a real-time implementation using dSPACE-1103based 1.6 kW BDFR machine prototype.

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Improved performance of motor drive using RBFNN-based hybrid reactive power MRAS speed estimator

Cited by 11 publications

References 10 publications

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

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

Sensorless Speed Control of AC Machines

Implementation of reactive power‐based MRAS for sensorless speed control of brushless doubly fed reluctance motor drive

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