Transient Vector Estimator Based High Dynamic Performance Control and Kalman Filter based Self Commissioning of Induction Motor Drives for Electric Vehicle Applications
Reddy, Siddavatam Ravi Prakash
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Induction machine, the work horse of the industry, has been widely used in industrial drives. The most common and efficient way to control the speed of induction machine is with variable frequency drives. The variable frequency drive schemes can be classiffied as scalar and vector control methods. Scalar control, which is based on the steady state ma- chine model, is a simpler and cost-effective way to control the speed of induction machine. Using scalar control, the amplitude and frequency of stator voltage are controlled with- out phase control, resulting in its poor dynamic response. This limits the usage of scalar control to the applications that do not require faster response. The advantage of scalar control is that it is not sensitive to the variation in machine parameters. In contrast to this, vector control that is based on the dynamic machine model, allows the independent control of torque and ux. The amplitude, frequency and phase of the stator voltage are controlled in vector control. The torque response in vector control is much faster when compared to that of scalar control techniques. However, vector control techniques are sensitive to the variation in machine parameters. Slip estimated in vector control scheme depends on the rotor resistance, rotor leakage and mutual inductances. Thus, either inaccurate estimation or variation in these parameters can lead to the incorrect estimation of slip, that results in the incorrect value of synchronous speed. This can lead to the degradation in the performance of vector control. A novel speed control scheme, that includes the advantageous features of both scalar and vector control schemes, is proposed. The proposed scheme provides good transient performance, while retaining the robustness aspects of scalar control. Scalar control acts as steady state platform in the proposed method. The transient response is improved by employing phase control of stator voltage as well. This is achieved through the addition of transient vector estimator to the scalar control. Slip in the proposed method is taken from the scalar control portion. Thus, the problems that arise due to the inaccurate estimation of slip can be overcome with the proposed method. Self commissioning is an important feature in the modern day electric drives. This allows the auto tuning of controllers, when high dynamic performance control schemes are employed. Performing no load and blocked rotor tests, is the conventional way for pa- rameter identification in induction machines. However, these tests require the mechanical blocking of rotor and thus it becomes more difficult to automatise these tests. Besides, the results obtained from these tests are not accurate. The self commissioning schemes reported in the literature assume a relation between stator and rotor leakage inductances. A self commissioning scheme that identifies all the six electrical parameters (stator resistance, rotor resistance, core loss resistance, stator leakage inductance, rotor leakage inductance and mutual inductance) is proposed. The estimation is done using Kalman filter algorithm, which is a recursive least squares algorithm. Pseudo random binary sequence (PRBS) signals are commonly used as input excitation in parameter estimation methods, as these very well meet the conditions of persistency of excitation. In the proposed method, sine triangle pulse width modulation (SPWM) signals are used as input excitation. This advantages is that, SPWM signals can be easily generated with voltage source inverters, when compared to that of PRBS signal generation. Generalised expression to evaluate core loss resistance at any given fundamental frequency, is also presented as a part of the proposed self commissioning scheme. Finally, the proposed speed control strategy and self commissioning schemes are practically implemented on open source electric vehicle platform.