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Abstract
Induction motors have high efficiency at rated speed and torque. However, at light loads, the efficiency decreases since iron losses increase dramatically. Since the efficiency improvement has a great effect on economic saving and reduction of environmental pollution, it is important to design an optimization technique to improve the efficiency of the induction motor.
So, this dissertation presents an innovative optimization controller for three-phase induction motor. In this controller, the Particle Swarm Optimization (PSO) is implemented to achieve optimum operation of the drive system (maximum efficiency, minimum stator current, maximum power factor, minimum operating cost,... etc.).
The optimization process uses Loss Model Controller (LMC) of the drive system and the Search Controller technique (SC). The main procedure of the Loss Model Controller is based on the modeling of the motor and the losses to derive an objective function. The objective function is optimized (either minimized or maximized) using the Particle Swarm Optimization (PSO). SC on the other hand measures the input power of the machine drive regularly at fixed time intervals and searches for the flux value which results in minimum power input for given values of speed and load torque.
In this dissertation, six strategies for optimal operation of induction motors are proposed. Those six strategies are based on the Loss Model Controller (LMC). In addition, a new hybrid Search Controller based on the Particle Swarm Optimization algorithm is developed. The hybrid method uniquely combines Loss Model and Search Control Techniques.
The proposed techniques are based on the principle that the flux level in a machine can be adjusted to optimize the fitness function for a given value of speed and load torque. The main advantages of the proposed technique are: simplicity of its structure, and its fastest convergence time compared to available techniques.