Modified Elman neural network control for PMSM direct-driven PMSG/Battery renewable energy system

Author

Chih-Hong Lin ; Ren-Jeng Wu

Author_Institution

Dept. of Electr. Eng., Nat. United Univ., Miao Li, Taiwan

fYear

2013

fDate

22-25 April 2013

Firstpage

651

Lastpage

656

Abstract

The modified Elman neural network (NN) controller to be used for the voltage control of the permanent magnet (PM) synchronous generator/battery renewable energy system is proposed to improve control performance of voltage adjustment. Because the PM synchronous generator/battery renewable energy system is a nonlinear time-varying system, three sets on-line trained modified Elman NN controllers are developed for the voltage tracking controllers of DC bus voltage of rectifier, AC voltage of inverter and DC voltage of battery storage system through boost/buck converter in order to improve output performance. Finally, experimental results are verified to show the effectiveness of the proposed control scheme.

Keywords

battery storage plants; invertors; machine control; neurocontrollers; nonlinear control systems; permanent magnet generators; power convertors; rectifiers; synchronous generators; time-varying systems; voltage control; DC bus voltage; PMSG; battery renewable energy system; battery storage system; boost-buck converter; inverters; modified Elman neural network control; nonlinear time-varying system; permanent magnet synchronous generator; rectifier; voltage adjustment; voltage control; voltage tracking controllers; Artificial neural networks; Batteries; Context; Inverters; Renewable energy sources; Voltage control; Voltage measurement; battery storage system; modified Elman neural network; permanent magnet synchronous generator;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Electronics and Drive Systems (PEDS), 2013 IEEE 10th International Conference on

Conference_Location

Kitakyushu

ISSN

2164-5256

Print_ISBN

978-1-4673-1790-0

Electronic_ISBN

2164-5256

Type

conf

DOI

10.1109/PEDS.2013.6527099

Filename

6527099