• PRASHANTH N A

In this paper, novel hybrid intelligent methods based on combined RBF neural network and Dynamic PSO (DYPSO-RBFNN), RBF neural network and Gaussian Mutation Strategy based self-adaptive Evolutionary programming(GMEP-RBFNN) have been developed and applied separately to optimize permanent magnet length bm and rotor slot opening bo simultaneously to maximize the linkage and mutual flux components and minimize the leakage flux component of PMSG. The use of self adaptive Gaussian mutation strategy has been applied to make the solution free from manual tuning of strategy parameters. The simultaneous estimation of permanent magnet length and rotor slot opening parameters provides the comfort in design process as well saving in the computation cost. The performance of GMEP has been compared with dynamic PSO to understand the relative benefits. It is observed that GMEP outperforms DYPSO in terms of maximizing the linkage and mutual flux components and minimizing the leakage flux component of PMSG. In terms of algorithm characteristics GMEP is having better and consistent convergence in compared to DYPSO.

Permanent magnet synchronous generators (PMSG),Dynamic Particle Swarm Optimization (DYPSO), Gaussian Mutation Strategy based self-adaptive Evolutionary programming (GMEP)

"Gaussian Mutation Strategy based self-adaptive Evolutionary programming to optimize the PMSG geometrical parameters.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.635-650, June-2019, Available :http://www.jetir.org/papers/JETIR1906238.pdf

"Gaussian Mutation Strategy based self-adaptive Evolutionary programming to optimize the PMSG geometrical parameters.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp635-650, June-2019, Available at : http://www.jetir.org/papers/JETIR1906238.pdf