Application of genetic algorithms to motor parameter determination for transient torque calculations

Abstract

This paper applies genetic algorithms to the problem of induction motor parameter determination. Generally available manufacturers published data like starting torque, breakdown torque, full load torque, full load power factor etc, are used to determine the motor parameters for subsequent use in studying machine transients. Results from several versions of the genetic algorithm are presented as well as a comparison with the Newton-Raphson method.

Multi-objective parameter estimation of induction motor using particle swarm optimization

“…In the recent years, global optimization techniques such as evolutionary algorithm (Nangsue et al, 1999), genetic algorithm (Bishop and Richards, 1990;Alonge et al, 1998;Pillay et al, 1997;Rahimpour et al, 2007;Huang et al, 2001;Nollan et al, 1994;Orlowska Kowalska et al, 2006), adaptive GA (Abdelhadi et al, 2004) and differential evolution (Ursem and Vadstrup, 2003) have been proposed to solve the parameter estimation problems.…”

“…Johnson and Willis (1991) and Cirrincione et al (2003) have applied deterministic approaches to the parameter estimation problem with some success, although with the inherent problem of convergence to a local optimum instead of the global minimum. The optimum determined by these techniques depends heavily on the initial guess of the parameter, with the possibility of a slightly different initial value causing the algorithm to converge to an entirely different solution (Pillay et al, 1997;Nangsue et al, 1999). Some approaches require derivative of the function, which is not always available or may be difficult to calculate.…”

New Method of Optimal Design of Electrical Rotating Machines

“…In [18], the optimization of the induction motor parameters is considered using the GA. In [19], the synchronous motor parameters are determined using the GA, the optimality criterion is the maximum torque. In [20], the method of the ERM size optimizing is implemented with the GA in the Ansoft Maxwell software package.…”

An Optimization of Design for S4-Duty Induction Motor Using Constraints Normalization Based Violation Technique

“…GA is ideally suited for unconstrained optimization problems (Pillay et al, 1997). As the present problem is a constrained optimization one, it is necessary to transform it into an unconstrained problem to solve it using GA (Sadish Sendil and Nagarajan, 2009).…”

“…Owing to the technological advancement in the computation power of computers the Genetic Algorithm (GA) find an efficient tool not only in electrical machines design (Subramanian et al, 2008;Subramanian et al, 2009;Hisu et al, 2003;Wieczorek et al, 1998;Wurtz et al, 1997;Pillay et al, 1997;Liuzzi et al, 2003), but also in many other applications like structural designs, sensor-actuator locations. One of the most important advantages of the GA over the standard NLP techniques is that it is able to find the global minimum, instead of a local minimum and that the initial attempts with different starting point need not be close actual values.…”