Torque and state estimation for real‐time implementation of multivariable control in sensorless induction motor drives

Merabet, Adel; Tanvir, Aman A.; Beddek, Karim

doi:10.1049/iet-epa.2016.0557

Cited by 17 publications

(10 citation statements)

References 29 publications

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“…It can be observed for the speed response in Figure 9a, a zero steady-state error, while a small steady-state error was observed in the speed tracking in reference [24], using the MRAS-sliding mode observer. Furthermore, the speed tracking occurred without an overshoot, compared to the result in reference [15], where a small overshoot was seen at the step transition as the speed estimation depended on the model parameters. In reference [22], the speed response had some oscillations, whereas these fluctuations were not observed in the speed tracking shown in Figure 9a.…”

Section: Resultsmentioning

confidence: 59%

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Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid

Tanvir

Merabet

2020

Energies

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This paper presents an improved estimation strategy for the rotor flux, the rotor speed and the frequency required in the control scheme of a standalone wind energy conversion system based on self-excited three-phase squirrel-cage induction generator with battery storage. At the generator side control, the rotor flux is estimated using an adaptive Kalman filter, and the rotor speed is estimated based on an artificial neural network. This estimation technique enhances the robustness against parametric variations and uncertainties due to the adaptation mechanisms. A vector control scheme is used at the load side converter for controlling the load voltage with respect to amplitude and frequency. The frequency is estimated by a Kalman filter method. The estimation schemes require only voltage and current measurements. A power management system is developed to operate the battery storage in the DC-microgrid based on the wind generation. The control strategy operates under variable wind speed and variable load. The control, estimation and power management schemes are built in the MATLAB/Simulink and RT-LAB platforms and experimentally validated using the OPAL-RT real-time digital controller and a DC-microgrid experimental setup.

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Section: Resultsmentioning

confidence: 59%

“…They are simple and require low computational effort. Conventional MRAS methods depend on the reference model accuracy, which is sensitive to the parametric variations and the overall control system [11][12][13][14][15]. Several solutions for enhancing MRAS performance have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid

Tanvir

Merabet

2020

Energies

Self Cite

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“…Meanwhile, it is a challenge for engineers to select the PI parameters by trial and error because of the large parameter uncertainties [2]. Thus, many approaches have been proposed for the motor drive system, such as, sliding mode variable structure control [3], feedback linearization control [4], finite-time control [5], predictive control [6], and passive control [7]. These methods may improve the performance of motor drive system in different aspects, such as good transient response, strong robustness, or lower torque ripple in the steady state.…”

Section: Introductionmentioning

confidence: 99%

Robust Current Predictive Control-Based Equivalent Input Disturbance Approach for PMSM Drive

Zhang

2019

Electronics

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The implementation and experimental validation of current control strategy based on predictive control and equivalent input disturbance approach is discussed for permanent magnet synchronous motor (PMSM) control system in the paper. First, to realize the current decoupling control, the deadbeat predictive current control technique is adopted in the current loop of PMSM. Indeed, it is well known that the traditional deadbeat current control cannot completely reject the disturbance and realize the zero error current tracking control. Then, according to the model uncertainties and the parameter variations in the motor, an equivalent input disturbance approach is introduced to estimate the lump disturbance in the system, which will be used in the feed-forward compensation. Thus, a compound current controller is designed, and the proposed algorithm reduces the tracking error caused by the disturbance; the robustness of the drive system is improved effectively. Finally, simulation and experiment are accomplished on the control prototype, and the results show the effectiveness of the proposed current control algorithm.

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“…A informação da velocidade rotórica de um motor de indução trifásico (MIT)é extremamente relevante para os mais diversos tipos de aplicações industriais, tais como nos controles de velocidade, posição, torque (Merabet et al (2017)), na estimativa do rendimento da máquina, ou ainda na detecção de eventuais falhas que podem comprometer a sua estrutura (Roque (2015)). O trabalho publicado por Lu et al (2006), por exemplo, realizou uma importante revisão da literatura e descreveu os principais métodos voltados para a avaliação de perdas e da eficiência nos motores de indução trifásicos conhecidos até aquele momento, sendo muitos deles empregados e/ou estudados até os dias de hoje.…”

Section: Introductionunclassified

Estimativa da Rotação em Motores de Indução Usando Histograma de Gradientes Orientados

Dias

Rodrigues

Silva

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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This paper presents the use of histogram of oriented gradients technique, usually applied in the image processing field, to estimate the slip in squirrel cage induction motors. Such value is achieved by monitoring only one machine phase current with sample time in 250ms, wich allows future real time applications. The features extracted with the histograms were used as inputs for a neural network to estimate the final rotation of the machine. The results obtained from some experimental tests are presented to validate the present approach. Resumo: O presente artigo aborda o uso da técnica de histograma de gradientes orientados, normalmente aplicada naárea de processamento de imagens, na estimativa da rotação em motores de indução trifásicos com rotor gaiola de esquilo. Tal grandezaé alcançada a partir do monitoramento de apenas uma das correntes de fase da máquina, empregando janelas amostrais de apenas 250 ms, o que corrobora para futuras aplicações em tempo real. As características extraídas com o apoio dos histogramas foram aplicadas em uma rede neural para a estimativa final da rotação de eixo da máquina. Os resultados obtidos a partir de alguns ensaios experimentais são apresentados no sentido de validar a abordagem proposta.

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Torque and state estimation for real‐time implementation of multivariable control in sensorless induction motor drives

Cited by 17 publications

References 29 publications

Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid

Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid

Robust Current Predictive Control-Based Equivalent Input Disturbance Approach for PMSM Drive

Estimativa da Rotação em Motores de Indução Usando Histograma de Gradientes Orientados

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