Swarm Intelligence Applications in Electric Machines

Particle Swarm Optimization (PSO) has potential applications in electric drives. The excellent characteristics of PSO may be successfully used to optimize the performance of electric machines in many aspects. In this chapter, a field-oriented controller that is based on Particle Swarm Optimization is presented. In this system, the speed control of two asymmetrical windings induction motor is achieved while maintaining maximum efficiency of the motor. PSO selects the optimal rotor flux level at any operating point. In addition, the electromagnetic torque is also improved while maintaining a fast dynamic response. A novel approach is used to evaluate the optimal rotor flux level by using Particle Swarm Optimization. PSO method is a member of the wide category of Swarm Intelligence methods (SI). There are two speed control strategies will demonstrate in next sections. These are field-oriented controller (FOC), and FOC based on PSO. The strategies are implemented mathematically and experimental. The simulation and experimental results have demonstrated that the FOC based on PSO method saves more energy than the conventional FOC method. In this chapter, another application of PSO for losses and operating cost minimization control is presented for the induction motor drives. Two strategies for induction motor speed control are proposed in this section. These strategies are based on PSO and called maximum efficiency strategy and minimum operating cost Strategy. The proposed technique is based on the principle that the flux level in a machine can be adjusted to give the minimum amount of losses and minimum operating cost for a given value of speed and load torque. In the demonstrated systems, the flux and torque hysteresis bands are the only adjustable parameters to achieve direct torque control (DTC) of induction motors. Their selection greatly influences the inverter switching loss, motor harmonic loss and motor torque ripples, which are the major performance criteria. In this section, the effects of flux and torque hysteresis bands are investigated and optimized by the particle swarms optimization technique. A DTC control strategy with variable hysteresis bands, which improves the drive performance compared to the classical DTC, is presented. Online Artificial Neural Networks (ANNs) could be also trained based on PSO optimized data. Here the fast response of ANN is used to optimize the operating conditions of the machine.