Comparison of sensorless techniques based on Model Reference Adaptive System for induction motor drives

Pinheiro, Diego Dias; Stein, Carlos M. O.; Costa, Jean Patric da; Cardoso, Rafael; Carati, Emerson Giovani

doi:10.1109/cobep.2015.7420106

Cited by 3 publications

(5 citation statements)

References 8 publications

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“…Similarly when lower switches S4, S6and S2 are ON (separately or together) this condition is represented by 0. * = * (7) So the only possible switching states combinations for the 3 leg inverter are 2 3 =8: 000, 001, 010, 011, 100, 101, 110, and 111. So 8 possible switching states are possible, out of which two of them are zero switching states ( 0 , 7 ) that produces zero output voltage and six of them are active switching states ( 1 − 6 ) that produces non zero output voltage.…”

Section: Space Vector Pulse Width Modulationmentioning

confidence: 99%

“…Both the models are used for the calculation of flux. The fluxes obtained from both the models are compared to produce an error signal which is responsible for the estimation of motor speed [5]- [7]. If the fluxes obtained from both the models is same i.e.…”

Section: Speed Estimation Using Mrasmentioning

confidence: 99%

“…Variation in stator resistance may result in poor performance of the drive particularly at low speed, while leakage reactance affects the performance in complete speed range. Thus this model depends upon the machine parameters for its operation [7].…”

Section: Introductionmentioning

confidence: 99%

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Sensorless Vector Control of Induction Motorusing Model Reference Adaptive System

Sahu¹,

Gupta²

2019

IJEET

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ABTRACTThe implementation and performance evaluation of sensorless vector control of three-phase induction motor is carried out in this paper which uses Model Reference Adaptive System (MRAS) technique for the estimation of motor speed. Sensorless control is basically a control technique in which speed estimation of motor is done without using speed sensor. Sensorless control increases the robustness, ruggedness of the system and reduces the cost, complexity, electromagnetic interference and maintenance required for the drive. MRAS technique is a widely used technique for speed estimation because of its simple implementation. In order to provide switching control signals to the legs of the IGBT's based two-level inverter; Space Vector Pulse Width Modulation (SVPWM) technique is used in this paper. This technique is based on the rotating space vectors. SVPWM provides better utilization of DC bus voltage and also the harmonic content and the switching losses gets reduced in this technique. The performance of MRAS technique used for speed estimation is evaluated for various values of load torque and speed. The simulation results show the feasibility and robustness of this method for high performance application.

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Section: Space Vector Pulse Width Modulationmentioning

confidence: 99%

Section: Speed Estimation Using Mrasmentioning

confidence: 99%

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Sensorless Vector Control of Induction Motorusing Model Reference Adaptive System

Sahu¹,

Gupta²

2019

IJEET

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“…The use of a mechanical speed sensor simplifies the implementation of the IFOC system. However, it makes the project more expensive due to the cost of the speed sensor and the other circuits for conditioning and instrumentation [18]. Physical speed sensors are associated with problems, such as mechanical robustness reduction of the drive, shaft extension, and cost increase.…”

Section: Introductionmentioning

confidence: 99%

A High-Performance Pll Observer for Sensorless Three-Phase Induction Motor Control

Carati¹,

Sant²,

Pinheiro³

2022

Eletrônica de Potência

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This paper presents an enhanced estimation algorithm based on the PLL (phase-locked loop) approach, which is used to estimate the rotor speed in induction motors (IM) drives. It can be challenging to obtain an accurate estimate during frequency ramps using existing PLL schemes.Thus, the performance of PLL schemes can be degraded during the acceleration and deceleration operations when applied to motor drives. In addition, the performance of conventional PLL scheme estimation is negatively affected by disturbances, for example, DC offsets. One of the novelties of the proposed speed observer (HPPO -High-Performance PLL Observer) is error normalization, which is based on the currents and a mechanism of variable gain based on the reference speed. The reference speed is also used to build an additional feedforward adjustment action. These modifications improve estimator results during load insertions and at low rates. In order to validate the proposed HPPO algorithm, it is implemented experimentally in a laboratory prototype using a 2.2kW IM. The motor is driven by a three-phase pulse width modulation (PWM) power converter, which is controlled by a DSP TMS320F28069. Numerical analysis and experimental results are carried out to validate the proposed scheme's high performance.

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“…Hence, the development of speed-identification schemes to replace the shaft position sensor is required. Several speedidentification schemes of FVC IMs have been published, including speed adjustment through an extended Kalman filter, (6)(7)(8) speed estimation derived from an adaptive control system scheme, (9,10) speed determination according to a flux estimator or the electromotive force of an IM, (11)(12)(13) and speed estimation from a neural network or fuzzy logic control. (14,15) In this study, model reference adaptive control (MRAC) was utilized to develop a synchronous speedidentification scheme based on the reactive power of an IM, and the estimated rotor speed was obtained by subtracting the slip speed from the estimated synchronous speed.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Speed Identification Air-gap Flux Vector-controlled Induction Motor Drive Based on Firefly Algorithm

Yung-Chang¹,

Lin²,

Pu³

2021

Sensors and Materials

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An adaptive speed-identification scheme based on the firefly algorithm (FA) is presented for an air-gap flux vector-controlled (AGFVC) induction motor (IM) drive. The AGFVC IM drive was established using a stator current and air-gap flux. The stator current was acquired from an IM using a Hall effect sensing element. The Hall effect element was employed as an electromagnetic sensor to detect the stator current while the AGFVC IM was rotating under different reversible steady-state speed commands. Model reference adaptive control (MRAC) was utilized to develop a synchronous speed-identification scheme based on the reactive power of the motor, and the rotor speed was estimated by subtracting the slip speed from the estimated synchronous speed. The MRAC adaptation mechanism was designed using the FA. The MATLAB/Simulink © toolbox was used to simulate the proposed AGFVC IM drive system, and all the control algorithms were implemented using a TI DSP 6712-and-F2812 control card to generate pulse width modulation signals through the power stage to actuate the motor. Both simulation and experimental results verified the effectiveness of the proposed system.

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Comparison of sensorless techniques based on Model Reference Adaptive System for induction motor drives

Cited by 3 publications

References 8 publications

Sensorless Vector Control of Induction Motorusing Model Reference Adaptive System

Sensorless Vector Control of Induction Motorusing Model Reference Adaptive System

A High-Performance Pll Observer for Sensorless Three-Phase Induction Motor Control

Adaptive Speed Identification Air-gap Flux Vector-controlled Induction Motor Drive Based on Firefly Algorithm

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