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ABSTRACT: In the high speed range, vector control of rotor flux orientation of an induction machine implements good performance. However, the performance in low speed rang deteriorates because of the inaccurate estimation of rotor flux and speed. In this paper, modified voltage model for rotor flux estimation and neuron model-reference adaptive system (MRAS) for speed estimation are used to improve the performance of speed sensorless vector control. To improve the accuracy of rotor flux estimation, the stator resistance is identified on-line. The experimental results show that the proposed scheme yields improved performance in low speed range.
Power Electronics and Drive Systems, 2007. PEDS '07. 7th International Conference on; 12/2007
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ABSTRACT: This paper discusses skid steering applied to four wheel drive electric vehicles. In such vehicles, steering is achieved by differentially varying the speeds of the lines of wheels on different sides of the vehicle in order to induce yaw. Skid steer wheeled vehicles require elaborate tire model, so author choose the unite semi-empirical tire model. From this model, longitudinal and lateral tire force can be calculated by slip ratio directly. The vehicle model has 3-DOF, longitudinal, lateral and yaw direction, irrespective of suspension. Induction motor is chosen as the driven motor, and the control method is rotor flux field oriented vector control. To satisfy the requirement of the turn radius, the longitudinal slip must be controlled, so a method of slip limitation feedback is used in the simulation. When the vehicle is turning on a slippery surface, because of the drop at the coefficient of road adhesion, the drive wheels may slip. The traction control system reduces the engine torque and brings the slipping wheels into the desirable skid range. Some simulation results about the steering accuracy and maneuverability are given in the paper.
Power Electronics and Drive Systems, 2007. PEDS '07. 7th International Conference on; 12/2007
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ABSTRACT: The development of wind generation has rapidly progressed over the last decade, but it must be integrated into power grids and electric utility systems. However, it cannot be dispatched like conventional generators because the power generated by the wind changes rapidly because of the continuous fluctuation of wind speed and direction. So it is very important to predict the wind power generation. This paper discusses why the wind power generation can be predicted in short-term, and how to setup the construction of an ANN (artificial neural network) prediction model of wind power based on chaotic time series. The analysis of modeling with low dimensions nonlinear dynamics indicates that time series of wind power generation have chaotic characteristics, and wind power can be predicted in short-term. Phase space reconstruction method can be used for ANN model design. The data from the wind farm located in the Saihanba China are used for this study.
Power Electronics and Drive Systems, 2007. PEDS '07. 7th International Conference on; 12/2007
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ABSTRACT: This paper presents a novel method to estimate the rotor flux
position of an induction machine by injecting a high frequency signal at
very low speed, including zero speed. When the high frequency
fluctuating signal is injected in the terminal of an induction machine,
the difference of impedance between the d-axis and q-axis of the
synchronous rotating reference frame can be measured. This phenomenon is
explained by a high frequency model of the induction machine, which is
oriented with a high frequency signal space vector. The rotor flux angle
can thus be tracked by tuning the space vector of the high frequency
signal. Experimental results are provided to verify that this algorithm
is robust against parameters variations and load changes
Electrical Machines and Systems, 2001. ICEMS 2001. Proceedings of the Fifth International Conference on; 09/2001
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ABSTRACT: A decoupling control approach based on alpha α-th inverse system has been developed for the innovative 5 degree-of-freedom bearingless induction motor, which is multi-variable, nonlinear and high coupling system. To start with, the working principles of innovative 5 degree-of-freedom bearingless induction motor is briefly introduced. Then the mechanical equations of 3 degree-of-freedom magnetic bearing and 2 degree-of-freedom bearingless induction motor are given. Also the state equations of the motor are set up. Secondly, the reversibility of system based on the mechanical equations is discussed. Using alpha α-th inverse system, the control between radial force and electromagnetic torque force and radial forces control are decoupled. Last but not least, the controllers are designed respectively according to the linear system theory. The simulation results have showed that the whole control system has good dynamic and static performance.
Control Conference, 2007. CCC 2007. Chinese;
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ABSTRACT: A decoupling control approach based on differential geometry has been developed for the bearingless induction motor, which is multi-variable, nonlinear and high coupling system. To start with, the mathematical equations of the bearingless induction motor describing the dynamic behavior of the radial force model have been transformed. Also the state equations of the motor are set up. Secondly, the theory of differential geometry is given and the suspension spring with variable stiffness instead of conventional one is used in the model. So the nonlinear model is transferred into a linear one. Last but not least, sliding model control theory has been applied to these subsystems to obtain the desired response. The simulation results have showed that the whole control system has good dynamic and static performance.
Control Conference, 2007. CCC 2007. Chinese;
