Sensorless control of a vector controlled three-phase induction motor drive using artificial neural network

Iqbal, Arif; Khan, Mohammad Rizwan

doi:10.1109/pedes.2010.5712474

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…Moreover, the use of ANN means the system has a faster dynamic speed, and greatly increases its robustness [63]. In [64], an ANN strategy was used to control the torque of the induction motor. The use of ANN led to a significant increase in the efficiency of the asynchronous motor compared to the DPC.…”

Section: Intelligent Dpc Strategy For Rscmentioning

confidence: 99%

Enhancement of Direct Power Control by Using Artificial Neural Network for a Doubly Fed Induction Generator-Based WECS: An Experimental Validation

et al. 2022

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Direct power control (DPC) is among the most popular control schemes used in renewable energy because of its many advantages such as simplicity, ease of execution, and speed of response compared to other controls. However, this method is characterized by defects and problems that limit its use, such as a large number of ripples at the levels of torque and active power, and a decrease in the quality of the power as a result of using the hysteresis controller to regulate the capacities. In this paper, a new idea of DPC using artificial neural networks (ANNs) is proposed to overcome these problems and defects, in which the proposed DPC of the doubly fed induction generators (DFIGs) is experimentally verified. ANN algorithms were used to compensate the hysteresis controller and switching table, whereby the results obtained from the proposed intelligent DPC technique are compared with both the classical DPC strategy and backstepping control. A comparison is made between the three proposed controls in terms of ripple ratio, durability, response time, current quality, and reference tracking, using several different tests. The experimental and simulation results extracted from dSPACE DS1104 Controller card Real-Time Interface (RTI) and Matlab/Simulink environment, respectively, have proven the robustness and the effectiveness of the designed intelligence DPC of the DFIG compared to traditional and backstepping controls in terms of the harmonic distortion of the stator current, dynamic response, precision, reference tracking ability, power ripples, robustness, overshoot, and stability.

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Section: Intelligent Dpc Strategy For Rscmentioning

confidence: 99%

Enhancement of Direct Power Control by Using Artificial Neural Network for a Doubly Fed Induction Generator-Based WECS: An Experimental Validation

et al. 2022

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“…Arif Iqbal and M. Rizwan Khan, 2010 [7], presented a new model reference adaptive system (MRAS) speed observer for high-performance field oriented control induction motor drives using neural networks. Performance analysis of speed estimator with the change in motor parameters especially resistances of stator and rotor is presented.…”

Section: Related Work Surveymentioning

confidence: 99%

Artificial Neural Estimator and Controller for Field Oriented Control of Three-Phase I.M.

Rashad¹,

Hassan²

2019

IJISA

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Speed control for an I.M is a few what complex strategies; the complexity is regularly increasing in line with the required system achievement. The main forms of control strategies are scalar, direct torque, adaptive, sensorless, and vector or Field Oriented Control (FOC). The FOC method is the most efficient technique in which machine parameters: Rotor flux, unit vector, and electromagnetic torque, usually are estimated by means of using Digital Signal Processing (DSP). The Artificial Neural Network (ANN) becomes an effective tool for controlling nonlinear device in present time. This paper proposes the using of ANN instead of DSP to estimate the machine parameters in order to reduce the hardware complexity and the Electromagnetic Interference (EMI) impact. Also, it presents the PI-NN controller which is based totally on ANN. The systems simulations for both DSP and ANN are depicted. The performance of the ANN-based system gives excellent results: overshot less than 0.5%, rise time 0.514 s, steady state error less than 0.2%, settling time 0.7 s. in conjunction with that of DSPbased performance: overshot about 2%, rise time 0.64 s, steady state error less than 0.4%, settling time 0.75 s.

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“…The estimator is also computationally intensive. An MRAS speed observer for high-performance FOC IM drives using ANN is proposed in [136]. The ANN structure of the observer contains the adaptive and constant weights and the adaptive weights are proportional to the rotor speed.…”

Section: Artificial Neural Network-based Mrasmentioning

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

190

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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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Sensorless control of a vector controlled three-phase induction motor drive using artificial neural network

Cited by 11 publications

References 10 publications

Enhancement of Direct Power Control by Using Artificial Neural Network for a Doubly Fed Induction Generator-Based WECS: An Experimental Validation

Enhancement of Direct Power Control by Using Artificial Neural Network for a Doubly Fed Induction Generator-Based WECS: An Experimental Validation

Artificial Neural Estimator and Controller for Field Oriented Control of Three-Phase I.M.

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

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