scholarly journals Observer based parallel IM speed and parameter estimation

2014 ◽  
Vol 11 (3) ◽  
pp. 501-521
Author(s):  
Sasa Skoko ◽  
Darko Marcetic ◽  
Veran Vasic ◽  
Djura Oros
Keyword(s):  
Parameter Estimation ◽  
State Vector ◽  
Electric Signal ◽  
Speed Estimation ◽  
Direct Connection ◽  
Thermal Drift ◽  
Rotor Speed ◽  
Rotor Flux ◽  
The Given ◽  
Detailed Presentation

The detailed presentation of modern algorithm for the rotor speed estimation of an induction motor (IM) is shown. The algorithm includes parallel speed and resistance parameter estimation and allows a robust shaft-sensorless operation in diverse conditions, including full load and low speed operation with a large thermal drift. The direct connection between the injected electric signal in the d-axis and the component of injected rotor flux were pointed at. The algorithm that has been applied in the paper uses the extracted component of the injected rotor flux in the d-axis from the observer state vector and filtrated measured electricity of one motor phase. By applying the mentioned algorithm, the system converges towards the given reference.

Full Order Observer for Speed Estimation of Vector Controlled Induction Motor Drive

2014 ◽  
Vol 698 ◽  
pp. 40-45
Author(s):  
Ahmed Zaki Diab ◽  
Denis Kotin ◽  
Vladimir Anosov ◽  
Oleg Slepcov
Keyword(s):  
Induction Motor ◽  
Dynamic Performance ◽  
Drive System ◽  
Speed Estimation ◽  
Rotor Speed ◽  
Model Reference Adaptive System ◽  
Rotor Flux ◽  
Wide Speed Range ◽  
Model Reference Adaptive ◽  
Digital Simulations

In this paper, full order observer speed estimation of a vector controlled induction motor is presented. To decrease the associated maintenance costs, the most conventional model reference adaptive system MRAS structure is used to estimate the rotor speed. The adaptive full-order observer based on IM equation is used to estimate stator currents and rotor flux. Lyapunov’s stability criterion is employed to estimate rotor speed. Synthesis of the controller has been described. Digital simulations have been carried out in order to evaluate the effectiveness of the proposed sensorless drive system. The results prove excellent steady-state and dynamic performance of the drive system in a wide speed range, which confirms validity of the proposed scheme.

scholarly journals Low Speed Estimation of Sensorless DTC Induction Motor Drive Using MRAS with Neuro Fuzzy Adaptive Controller

2018 ◽  
Vol 8 (5) ◽  
pp. 2691
Author(s):  
Mini R ◽  
Shabana Backer P. ◽  
B. Hariram Satheesh ◽  
Dinesh M. N
Keyword(s):  
Comparative Analysis ◽  
Induction Motor ◽  
Reactive Power ◽  
Adaptive Controller ◽  
Operating Conditions ◽  
Estimation Methods ◽  
Speed Estimation ◽  
Loop Model ◽  
Low Speed ◽  
Rotor Flux

<p>This paper presents a closed loop Model Reference Adaptive system (MRAS) observer with artificial intelligent Nuero fuzzy controller (NFC) as the adaptation technique to mitigate the low speed estimation issues and to improvise the performance of the Sensorless Direct Torque Controlled (DTC) Induction Motor Drives (IMD). Rotor flux MRAS and reactive power MRAS with NFC is explored and detailed analysis is carried out for low speed estimation. Comparative analysis between rotor flux MRAS and reactive power MRAS with PI as well as NFC as adaptive controller is performed and results are presented in this paper. The comparative analysis among these four speed estimation methods shows that reactive power MRAS with NFC as adaptation mechanism shows reduced speed estimation error and actual speed error at steady state operating conditions when the drive is subjected to low speed operation. Simulation carried out using MATLAB-Simulink software to validate the performance of the drive especially at low speeds with rated and variable load conditions.</p>

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

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1743 ◽  
Author(s):  
Aman A. Tanvir ◽  
Adel Merabet
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Kalman Filter ◽  
Wind Energy ◽  
Power Management ◽  
Induction Generator ◽  
Rotor Speed ◽  
Dc Microgrid ◽  
Control Scheme ◽  
Rotor Flux

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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