Skate 3 Para Pc Utorrent 42
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In this technique, the position information can be detected on the basis of the conducting state of free-wheeling diodes connected in antiparallel with power transistors because a current flows in a phase. In this phase any active drive signal is given to the positive and negative side transistors and the current results from the back-EMFs produced in the motor windings. The three-phase permanent magnet synchronous motor has the trapezoidal back-EMFs shown in Figure 9. To produce the maximum torque, the inverter commutation should be performed every 60 so that the rectangular-shaped motor line current is in phase with the back-EMF signal. A starting circuit is needed to give a commutation signal for starting. This approach makes it possible to detect the rotor position over a wide speed range, especially at a lower speed, and to simplify the starting procedure [19].
As the back-EMF signal is sampled at the end of PWM off time, if the PWM duty cycle is high enough such that the off time is less than the falling edge time, the sampling result is not correct because the discharging period has not finished yet. To shorten the discharging time, the RC time constant should be reduced. A possibility is to use a smaller resistor in parallel with the resistor of the time constant, and a diode to block the charging current passing through the parallel resistor [49].
Apart from the variation of the stator resistance with temperature, other parameters in the AFFO will change during operation as well, such as the rotor resistance due to temperature changes, which will have an important influence on the speed accuracy of the adaptive observer. The stator and rotor self-inductance and magnetizing inductance vary due to magnetic saturation, being it possible to use a nonlinear magnetic model. In steady state, it is known that a misestimation of the rotor resistance provides correct estimations of the stator and rotor flux, but results in a misestimation of the speed [69].
Referring to motors, ANNs can be used to estimate rotor flux, unit vector, and torque in vector-controlled drives. The network has to be trained with a very large number of simulation data sets, so DSP-based estimators and ANN-based estimators perform comparably [73]. The capability of a neural network can be deployed to have online estimators to address the situation of similar disturbances in both stator and rotor resistances simultaneously. In this situation, the resistance observer can be realized with a recurrent neural network trained using the standard back-propagation learning algorithm. The block diagram of a rotor flux oriented induction motor (IM) drive together with both stator and rotor resistance identifications are shown in Figure 20, which also applies to BLDC motors. A rotor flux oriented vector controller commands the motor, and the voltage model fluxes are estimated from the measured stator voltages and currents using a programmable cascaded low-pass filter (PCLPF). The stator voltages are PWM voltages, which are filtered, and only the sinusoidal voltages are taken into the PCLPF flux estimators. The RRE and SRE blocks estimate the rotor and the stator resistances, respectively, taking into consideration that the stator resistance estimation depends on the rotor resistance. The rotor flux linkages are compensated by the RRE and hence the estimation error in the stator resistance is avoided [75].
A brushless DC motor built in a compressor of air conditioner is a typical application and it confirms the feasibility and the validity of some sensorless algorithms, such as a variation of the back-EMF zero crossing detection method. It is necessary to modulate the capacity of room air conditioners in proportion to the load results in energy saving and a comfortable environment. The speed of the brushless motor with a permanent magnet rotor can be easily controlled over a wide range by changing the motor voltage. Nevertheless, this type of motor needs a rotor position sensor, and this reduces the system ruggedness and complicates the motor configuration. In particular, the motor speed control and elimination of mechanical sensors are the main points of the sensorless methods, which contribute to the motor built in a completely sealed compressor and make it possible to mass produce. Mechanical sensors have low reliability in high-temperature and the need of a extra hermetic terminal of the sensor signal lead wires, and can be substituted by low-pass filters and voltage comparators [1].
Hydraulic systems are commonly used in automotive applications, since they allow developing higher forces and torques compared with purely electric actuators. For example, passenger cars are equipped with hydraulically-assisted brakes, clutches and power steering systems, while in commercial vehicles hydraulic power is used to operate also lifting systems and other auxiliary machineries. If a variable speed electric motor is coupled to the hydraulic pump, a flow control valve is always necessary, and the possibility to regulate the rotational speed of the pump independently from the engine speed allows a significant reduction of parasitic losses. However, this solution requires the design of a specific electronic controller, capable of motor speed regulation according to the hydraulic load dynamic requirements. Moreover, since the power supply of these electro-hydraulic systems must be the battery of the vehicle, to allow operation even when the engine is switched off, electric motors and power electronics must be designed for low-voltage and high-current ratings, especially in commercial vehicle applications [4]. Then, a brushless motor are the appropriate device to this purpose. 153554b96e
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