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Optimal Design of Tidal Power Generator Using Stochastic Optimization Techniques
Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, Department of Electrical Power Engineering.
2014 (English)MasteroppgaveStudent thesis
Abstract [en]

Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are used to reduce the cost of a permanent magnet synchronous generator with concentrated windings for tidal power applications. Reducing the cost of the electrical machine is one way of making tidal energy more competitive compared to traditional sources of electricity. Hybrid optimization combining PSO or GA with gradient based algorithms seems to be suited for design of electrical machines. Results from optimization with Matlab indicate that hybrid GA performs better than Hybrid PSO for this kind of optimization problems. Hybrid GA shows better convergence, less variance and shorter computation time than hybrid PSO. Hybrid GA managed to converge to an average cost of the generator that is 5.2 % lower than what was reached by the hybrid PSO. Optimization results show a variance that is 98.6 % lower for hybrid GA than it is for hybrid PSO. Moving from a pure GA optimization to the hybrid version reduced the average cost 31.2 %. Parallel processing features are able to reduce the computation time of each optimization up to 97 % for large problems. The time it took to compute a GA problem with 2500 individuals was reduced from 12 hours to 21 minutes by switching from a single-processor computer to a computer with 48 processor cores. The run time for PSO with 400 particles and 100 iterations went from 18.5 hours to 74 minutes, a 93 % reduction.

Place, publisher, year, edition, pages
Institutt for elkraftteknikk , 2014. , 29 p.
Keyword [no]
ntnudaim:11263, MTENERG energi og miljø, Elektrisk energiteknikk
URN: urn:nbn:no:ntnu:diva-27239Local ID: ntnudaim:11263OAI: diva2:762912
Available from: 2014-11-13 Created: 2014-11-13

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