Interpolation Algorithm Application Using Neural Networks for Flywheel Energy Storage System

Shao Song Wan; Jian Cao; Qun Song Zhu; Cong Yan

doi:10.4028/www.scientific.net/AMR.989-994.2503

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Improving Performance of Wireless-Optical Broadband Access Network by Interpolation Algorithm of Zadoff-Chu Sequence
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Interpolation Algorithm Application Using Neural Networks for Flywheel Energy Storage System
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Optimization Model and Algorithm for Responsive Closed-Loop Supply Chain Logistics Network with Direct Shipment under E-Commerce
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994Interpolation Algorithm Application Using Neural...

Interpolation Algorithm Application Using Neural Networks for Flywheel Energy Storage System

Abstract:

This paper presented the key techniques development of motor/generator (M/G) for the FES system in recent years, and summarized the latest developments of three machines such as: induction machine (IM), reluctance machine (RM) and permanent magnet machine (PMM) including traditional structure PMM and Halbach structure PMM, which now widely used in the FES system. The characteristics of three machines had been compared and analyzed, which provides guidance for the FES field researchers to choose appropriate M/G.Then the comparison of spectra of the sequences respectively interpolated by FDIA and commonly used interpolation algorithm is conducted, which affirms the superiority of FDIA. Finally, an effective method of achieving best decimation of the sequence interpolated by FDIA is provided. All these lay primary theory foundation for the application of Zadoff-Chu sequence to HF channel sounding.

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Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

2503-2506

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.2503

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Online since:

July 2014

Authors:

Shao Song Wan*, Jian Cao, Qun Song Zhu, Cong Yan

Keywords:

Flywheel Energy Storage, Interpolation, NN, Reluctance Machine

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