Maximum wind power tracking based on cloud RBF neural network-Reference-Cited by-同舟云学术

Maximum wind power tracking based on cloud RBF neural network

Published:2016-02 Issue: Volume:86 Page:466-472
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Wu Zhong-Qiang,Jia Wen-Jing,Zhao Li-Ru,Wu Chang-Han

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference15 articles.

1. Wind characteristics and wind energy potential in western Nevada;Belu;Renew. Energy,2009

2. Energy and reliability benefits of wind energy conversion systems;Xie;Renew. Energy,2011

3. Improvement of power quality for PMSG wind turbine systems;Nguyen;ECCE Trans Energy Convers. Congr. Expo.,2010

4. Suboptimal control based on hill-climbing method for time delay systems;Santos;Control Theory Appl.,2007

5. Maximum power point tracking and grid feeding of permanent magnet synchronous generator based wind energy conversion system using modified hill climb searching algorithm;Linus;IEEE Trans Power Electron.,2012

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