Development of an Improved Particle Swarm Optimization (PSO) and its Application to Induction Motor Soft-Starting
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This paper reports the design and testing of a soft starter for induction motor drives. The motor current regulation during starting is formulated as an optimization problem and the solution is obtained through an improved version of Particle Swarm Optimization (PSO). The enhancement in standard PSO is obtained by suitably integrating three attributes, namely probabilistic approach, Elitism and population modification borrowed from other existing biologically inspired optimization techniques such as Genetic Algorithm (GA), Bees GA, Artificial Immune System (AIS) etc. In the improved version, at the end of one iteration, the best particle is retained and the rest of the population is replaced by newly generated particles; this replacement is based on a probability. The new PSO is first tested on a set of standard test functions and after ascertaining its superiority, the proposed PSO is employed for soft starting of induction motor. The optimised values of Proportional-Integral (PI) controller obtained through the new PSO when tested on the d-q axis model of the squirrel cage induction motor, shows enhanced starting dynamics. The theoretical predictions on the motor are validated through extensive experimental results
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