The optimization of electrical machines can be managed using advanced computational intelligence algorithms. These algorithms can speed up the design phase and can improve the performances, being able to find out the optimal design also in problems involving a large number of physical and geometric parameters. In this paper, a new population based metaheuristic algorithms, named Social Network Optimization (SNO), has been used to find the optimal design of a tubular permanent magnet linear generator (TPMLG), in the context of a vehicular energy harvesting system.

Optimized linear generator for vehicle energy harvesting by social network optimization algorithm

F. Grimaccia;G. Gruosso;M. Mussetta;A. Niccolai;R. E. Zich
2017-01-01

Abstract

The optimization of electrical machines can be managed using advanced computational intelligence algorithms. These algorithms can speed up the design phase and can improve the performances, being able to find out the optimal design also in problems involving a large number of physical and geometric parameters. In this paper, a new population based metaheuristic algorithms, named Social Network Optimization (SNO), has been used to find the optimal design of a tubular permanent magnet linear generator (TPMLG), in the context of a vehicular energy harvesting system.
IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
9781538611272
Generators;Mathematical model;Optimization;Social network services;Stator cores;Stator windings;Energy Harvesting;Road models;Social Network optimization;Tubular Linear Generator;Vehicle models
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1040363
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