Veran Vasic

A stator resistance estimation scheme for speed sensorless rotor flux oriented induction motor drives

Accurate knowledge of stator resistance is of utmost importance for correct operation of a number of speed sensorless induction motor control schemes in the low speed region. Since stator resistance inevitably varies with operating conditions, stable and accurate operation at near-zero speed requires an appropriate online identification algorithm for the stator resistance. The paper proposes such an identification algorithm, which is developed for the rotor flux-based model reference adaptive system (MRAS) type of the speed estimator in conjunction with a rotor flux oriented control scheme. In this speed estimation method, only one degree of freedom (out of the two available) is utilized for speed estimation. It is proposed to utilize the second available degree of freedom as a means for adapting the stator resistance online. The parallel stator resistance and rotor speed identification algorithm is developed in a systematic manner, using Popovs hyperstability theory. It increases the complexity of the overall control system insignificantly and enables correct speed estimation and stable drive operation at near-zero speeds. The proposed speed estimator with parallel stator resistance identification is at first verified by simulation. Extensive experimentation is conducted next at low speeds of rotation and successful stator resistance identification is achieved down to 0.5-Hz frequency of rotation.

Optimal MRAS speed estimation for induction generator in wind turbine application

This paper propose improved sensorless vector control of squiral cage induction generator for variable speed wind energy conversion system. The rotational speed of the induction generator is estimated with the Model Reference Adaptive System - MRAS observer. The estimated rotational speed is used as the feedback of the control loop in the converter control system. Proportional integral controller in the MRAS observer is optimized via Genetic Algorithm, Partical Swarm Optimization and Simulated Annealing. Comparative analise of the optimal speed estimation of induction generator is also presented. The performance of the sensorless controled variable speed wind turbine drive is evaluated through simulation in Matlab/Simulink. Experimental results are gained via laboratory model based on dSpace DS1104 digital control card.

Natural Field Orientation Sensorless Induction Motor Drive with On-line Stator Resistance Parameter Update

Abstract This article presents a new technique for on-line identification of an induction motor stator resistance parameter. The technique is designed as an upgrade of the natural field orientation system in a shaft-sensorless indirect field-oriented control induction motor drive. The proposed upgrade results in simultaneous rotor speed estimation and stator resistance parameter update, improving natural field orientation scheme robustness. The parameter update is based on information available in the d-axis back-emf component, investigated in detail using a steady-state model of potentially detuned natural field orientation scheme. The effectiveness of the parameter update technique is validated via practical experiments under a variety of conditions.

Broken bar detection using current analysis — A case study

This paper covers a case study of broken bar detection for 3.15 MW motor in a thermal power plant application. The motor current is measured in one phase. Feature extraction is based on transient and steady state analysis. Hilbert and Wavelet transforms are used to extract broken bar features. To discuss rotor condition in time domain skewness and kurtosis of current envelope are also considered. Low shaft-load conditions are present. In case of high-voltage, high-power induction motor reliable broken bar detection is possible when contemporary digital signal processing techniques are used.

GA optimization of PI controller in MRAS structure for induction motor speed estimation

This paper represents genetic algorithm optimization of PI controller parameters in model reference adaptive system structure for speed estimation of induction motor. Experiments were taking out via dSPACE DC1104 digital control card at laboratory experimental drive. For needs of simulation and model building Matlab/Simulink, software was used. Experimental results show that known theoretical and experimental foundations published by various authors support used methodology.

Sensorless Vector Control and Effects of Machine Parameters Mismatch in Variable Speed Wind Turbines

This paper discusses the development of model sensorless vector control of induction machine used in wind turbine application. The paper presents preliminary results. Instead of wind turbine, induction machine equipped with a frequency converter is used to simulate wind behaviour. In order to estimate the shaft speed of the induction machine model reference adaptive system (MRAS observer) is used. It is shown that parameters mismatch, at most rotor winding resistance has a great influence on speed estimation using MRAS. Simulation results were carried out in Matlab Simulink. At the end the experimental are shown in order to verify the results obtained by the computer simulation.

The estimation of iron losses in a non-oriented electrical steel sheet based on the artificial neural network and the genetic algorithm approaches

Cold rolled non-oriented (CRNO) electrical steel sheets are soft ferromagnetic materials which are commonly used for electromagnetic core design for AC rotating electrical machines. When these materials are exposed to time-varying magnetic fields, the iron losses occur. These losses represent the power dissipated in the ferromagnetic material and they are dependent upon the frequency and magnetic flux density level of the applied time-varying magnetic field. In order to achieve high-efficiency electrical machines, especially at high operating frequencies and magnetic flux density levels, iron losses should be kept as low as possible. This imposes the need for more accurate iron losses models, but also for fast and reliable estimation techniques. This paper considers the applications of an artificial neural network (ANN) and a genetic algorithm (GA), based on the classical iron losses separation formulation for a fast estimation of the specific iron losses in CRNO electrical steel sheet grade M530-50A over a wide frequency and magnetic flux density range. Iron losses measurement data, provided by the manufacturer, are used to calibrate the iron losses models. The approaches were verified using the manufacturers measurement data. Acceptable accuracy was obtained.

Prediction of local instabilities in open‐loop induction motor drives

Purpose – The purpose of this paper is to propose an analytical method for prediction of self‐sustained oscillations that might happen during low‐cost induction motor drive application. This forecast is needed to avoid unwanted oscillations that can be encountered for in fan, compressor and pump drives utilizing open‐loop frequency‐controlled three‐phase induction motor drives.Design/methodology/approach – The paper presents the model of the induction motor drive system that includes inverter switches dead‐time and allows discontinuous current of front‐end rectifier. Stability analysis of proposed model was performed by tracing the eigenvalues of the overall system matrix.Findings – Discontinuous rectifier current at light loads and the dead‐time of the inverter switches are the main sources of undesired low‐frequency self‐sustained speed oscillations in open‐loop controlled induction motor drives. The evaluated risk prediction is a function of drive and motor parameters and load level.Originality/value –...