Recurrent neural network based adaptive integral sliding mode power maximization control for wind power systems-Reference-Cited by-同舟云学术

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Recurrent neural network based adaptive integral sliding mode power maximization control for wind power systems

Published:2020-01 Issue: Volume:145 Page:1149-1157
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Yin Xiuxing,Jiang Zhansi,Pan Li

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangxi Province

Natural Science Foundation of Zhejiang Province

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference25 articles.

1. Short-term load and wind power forecasting using neural network-based prediction intervals;Quan;IEEE Trans. Neural Netw. Learning Syst.,2014

2. Fuzzy-logic sliding-mode control strategy for extracting maximum wind power;Yin;IEEE Trans. Energy Convers.,2015

3. A review of maximum power point tracking algorithms for wind energy systems;Abdullah;Renew. Sustain. Energy Rev.,2012

4. Comprehensive review of wind energy maximum power extraction algorithms;Shravana MusunuriH;Power Energy Soc. Gen. Meet.,2011

5. An interval-valued neural network approach for uncertainty quantification in short-term wind speed prediction;Ak;IEEE Trans. Neural Netw. Learning Syst.,2015

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