In recent years linear generators have been proposed in several marine applications as a well-suited technology for power generation such as power buoys. Those kinds of buoys could be used as large-scale devices in power plants and as small-size devices in electronic supply as well. In this work a three phase tubular permanent magnet linear generator is analyzed to supply electronic devices such as sensorial buoys with energy scavenging. A soft-computing algorithm based on a finite element method has been developed and a parametric approach is presented in order to provide a first optimization for electronic applications. Numerical results have been reported and discussed in detail

Recently the technology development and increasing amounts of investment in renewables has led to a growing interest towards design and optimization of green energy systems. In this context, advanced Computational Intelligence (CI) techniques can be applied by engineers to several technical problems in order to find out the best structure and to improve efficiency in energy recovery. This research promises to give new impulse to using innovative unconventional renewable sources and to develop the so-called Energy Harvesting Devices (EHDs). In this paper, the optimization of a Tubular Permanent Magnet-Linear Generator for energy harvesting from vehicles to grid is presented. The optimization process is developed by means of hybrid evolutionary algorithms to reach the best overall system efficiency and the impact on the environment and transportation systems. Finally, an experimental validation of the designed EHD prototype is presented.

In this paper the optimization of a Tubular Permanent Magnet-Linear Generator (TPM-LiG) for energy generation is presented. The application is related to the sea wave energy generation for small sensorized buoy. The optimization process is developed by means of an hybrid evolutionary algorithm widely presented in the paper. The advantage of this algorithm is in the wide exploration of the variables space and in the effective exploitation of the fitness function. The algorithm has been tested on a benchmark case and then applied to the optimization of the here considered device

In recent years, the increase in computational capability and development of innovative multiphysic techniques has determined a growing interest toward modeling and optimization in engineering system design for green energy applications. In this field, advanced soft computing techniques can be applied by engineers to several problems and to be used in an optimization process to find out the best design and, thus, to improve the system performance. These techniques also promise to give new impulse to research on renewable systems and, particularly in the last five years, on the so-called energy-harvesting devices (EHDs). This paper presents the optimization of a tubular permanent-magnet linear generator used for applications of energy harvesting from traffic. The optimization process is developed by means of hybrid evolutionary algorithms to reach the best overall system efficiency and the impact on the environment and transportation systems. Finally, an experimental validation of the designed EHD prototype is presented