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Derivation of Improved Induction Machine Model

Christian C. Onwuso, Sepiribo L. Braide, Hachimenum N. Amadi, Kingsley O. Uwho

Abstract


Several uses of induction motors (IMs) are as a result of variation in speed according to the load demand. Being non linear machines, they have high dynamic interactions. This work seeks to improve an induction motor by using vector control approach. This method removes the coupling effects (using a frame of reference like synchronous, stationary) which makes the response sluggish. Before now, PI controllers have been utilized for the purpose of speed regulation but the process of tuning these controllers consumes time due to the need for adjusting its parameters whenever a parameter is reversed. Hence, to avoid this, fuzzy controllers are used since improving the efficiency of the induction motor is always a major concern. Various essential strategies are adopted to ensure optimum efficiency operation of induction motor drive. Before now, differs control strategies such as direct torque control,  sensorless control , vector or field oriented control. Search and loss model controllers are used to optimize the efficiency of proposed machine model. Search controller is a very potent feedback method that finds optimal efficiency by adopting a search technique. Loss model controller relies on motor parameters in that the objective functions are gotten from modelling of the motor and its losses which are optimized to yield maximum efficiency. Machines are designed to operate at rated flux because it gives fast transient response and high torque to ampere ratio. If rated flux is maintained at light loads, then core losses of the machine will be excessively high which will result in reduced efficiency. MATLAB/SIMULINK is used to examine the effectiveness of the control mechanism. From the results, no overshoots and undershoots are seen in the proposed model hence, it gives good dynamic performance. Loss model control works well in hysteresis current controller compared to SVPWM. On using optimization algorithm, the efficiency of the motor is increased by 10% at low load (20% of the rated torque).


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References


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