Indirect Rotor Field Orientation Vector Control for Induction Motor Drives in the Absence of Current Sensors

Wang, Z.S.; Ho, Siu Lau

doi:10.1109/ipemc.2006.4778332

Cited by 13 publications

(5 citation statements)

References 7 publications

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“…The induction motor drive is operated with a rotor flux orientation controller. A multi-level low-pass filter is used for the estimation of voltage model fluxes from the measured stator voltages and currents [51,52]. The induction motor parameters are obtained from no-load and blocked rotor tests performed in the laboratory as presented in Section 4.1.…”

Section: Modeling Analysismentioning

confidence: 99%

Novel Advanced Artificial Neural Network-Based Online Stator and Rotor Resistance Estimator for Vector-Controlled Speed Sensorless Induction Motor Drives

Kanakabettu,

Irvathoor,

Saralaya

et al. 2024

Energies

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This paper presents a new approach for the online estimation of stator and rotor resistance of induction motors for speed sensorless vector-controlled drives, using feed-forward artificial neural networks with advanced adaptive learning rates. For the rotor resistance estimation, a neural network model based on rotor speed and stator currents is developed. The rotor flux linkages acquired from the voltage model are compared with the neural network model. The feed-forward neural network employs an adaptive learning rate as the function of the obtained error during training for quick convergence with minimal estimation error. A two-layered neural network model based on the stator voltage and current equations is developed for the stator resistance estimation. The d-q axes stator currents obtained from the developed model are compared with the acquired d-q axes stator currents. For the fast convergence with minimal estimation error, an adaptive learning rate as the function of error is adopted during training. Furthermore, the neural network estimates the induction motor’s speed. The simulation and experimental results justify that the developed algorithms track variation in the resistances quickly and precisely along with the speed as compared with the conventional constant learning rate algorithm, leading to reliable operation of the drive.

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Section: Modeling Analysismentioning

confidence: 99%

Novel Advanced Artificial Neural Network-Based Online Stator and Rotor Resistance Estimator for Vector-Controlled Speed Sensorless Induction Motor Drives

Kanakabettu,

Irvathoor,

Saralaya

et al. 2024

Energies

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“…An ERGM is a representation from this group which elaborates networks ceremonially casual graph exist of a place of nodes and edges position of the nodes are linked and if not [15]. These look alike shows a slate of being probable distribution on each feasiblenetwork on nodes [17], [18]. Nevertheless, expanse to put of attainable networks for a simple graph of size [14], [15].…”

Section: Introductionmentioning

confidence: 99%

“…To the equivalent of transforming electrical input into mechanical output with valid elementary functions in network gives product of vector identification in starting condition[18],[19]. A circuit with unbalanced and balanced losses is varied inobservance of analysis in induction motor fallowing with relation of voltage, current and power factor.…”

mentioning

confidence: 99%

Elementary Functions Module Derivative in Liouvilles Vectors for Induction Motors with Exponential Random Graphical Networks

Kiran¹,

Haritha²

2018

IJEEE

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Derivative functions in specific with complex module gives the development of induction motor at start position. in describing the torque of induction motor at position 1,2and 3 a vector control with exponential systems in liouvilles principle gives equality of defining stages in elementary calculation carrying start torque with function of z is derived with positive nth root convection which required the exp(z) as given in Euler's method. Conversation of singularities from 2 and 3 stages complex powers with non-zero sections in logarithmic properties of poles and zeros. initiation of logarithmic complex numbers with function accordance in formulating increases poles and zeros version at tst and tmax in induction motor by carrying slip range to running mode. Considering liouvilles vector a mathematical solution is derived with geometrical structures. A wavelet function of trapezoidal, sinusoidal is calculated in running condition along with stand still position of induction motor. Variable frequency of drives in exponentials with algebraic gives 20% of energy saving in semiconductor drives at industry applications .

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“…In this method the motor parameter variations are considered as negligible with the change in temperature and flux saturation. In [3] simplified vector control implementation strategy has been presented with absence of current sensors The limitation of this method is as there is no voltage and current feedback the performance of the drive may deteriorate due to parameter variation and disturbance in the input dc link voltage. A neural network based indirect vector control scheme for a three phase induction motor is presented in [4].…”

Section: Introductionmentioning

confidence: 99%

Type-2 Fuzzy logic based controllers for indirect vector controlled SVPWM based two-level inverter fed induction motor drive

Abhiram¹,

Prasad

2014

2014 IEEE 6th India International Conference on Power Electronics (IICPE)

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SVPWM algorithm is used to control a voltage source inverter fed indirect vector controlled induction motor drive to have optimum use of the dc bus voltage and maximum output torque. In indirect vector control method, Type-2 fuzzy logic based speed and current controllers replace the conventional PI based speed and current controllers to achieve good dynamic performance, to minimize the torque ripple during transient and steady state conditions. For Type-2 fuzzy set the third facet Footprint of Uncertainty (FOU) provides additional degree of freedom to handle uncertainties improving the dynamic performance of induction motor. The performance of the drive is investigated at no-load and step change in load. The THD values of line voltage and stator current of motor along with source current THD and the device current ripple of the two-level inverter are compared between conventional PI and Type-2 Fuzzy logic based controllers.

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Indirect Rotor Field Orientation Vector Control for Induction Motor Drives in the Absence of Current Sensors

Cited by 13 publications

References 7 publications

Novel Advanced Artificial Neural Network-Based Online Stator and Rotor Resistance Estimator for Vector-Controlled Speed Sensorless Induction Motor Drives

Novel Advanced Artificial Neural Network-Based Online Stator and Rotor Resistance Estimator for Vector-Controlled Speed Sensorless Induction Motor Drives

Elementary Functions Module Derivative in Liouvilles Vectors for Induction Motors with Exponential Random Graphical Networks

Type-2 Fuzzy logic based controllers for indirect vector controlled SVPWM based two-level inverter fed induction motor drive

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