Wind turbine rotor speed design optimization considering rain erosion based on deep reinforcement learning-Reference-Cited by-同舟云学术

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Wind turbine rotor speed design optimization considering rain erosion based on deep reinforcement learning

Published:2022-10 Issue: Volume:168 Page:112788
ISSN:1364-0321
Container-title:Renewable and Sustainable Energy Reviews
language:en
Short-container-title:Renewable and Sustainable Energy Reviews

Author:

Fang Jianhao,Hu Weifei,Liu Zhenyu,Chen Weiyi,Tan Jianrong,Jiang Zhiyu,Verma Amrit Shankar

Funder

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

State Key Laboratory of Fluid Power and Mechatronic Systems

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference47 articles.

1. Repair of wind turbine blades: review of methods and related computational mechanics problems;Mishnaevsky;Renew Energy,2019

2. Materials for wind turbine blades: an overview;Mishnaevsky;Materials,2017

3. Optimum sizing of an autonomous wind–diesel hybrid system for various representative wind-potential cases;Kaldellis;Appl Energy,2006

4. Lazard's levelized cost of energy analysis—version 13.0;Ray,2019

5. Rain erosion properties of materials;Busch,1966

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4. Fuzzy-based collective pitch control for wind turbine via deep reinforcement learning;ISA Transactions;2024-05

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