Applications of extreme learning machine based neural network on wind turbine pitch angle control
Benefited from the advancement of modern science and technology, the wind energy has become an import source for electricity generation. However, the fluctuating nature of the wind has been a bottleneck for its widely application for years. In this report, a control strategy is designed to guarantee...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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2010
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| Online Access: | http://hdl.handle.net/10356/39498 |
| _version_ | 1826120521248407552 |
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| author | Zhou, Kai. |
| author2 | Wang Youyi |
| author_facet | Wang Youyi Zhou, Kai. |
| author_sort | Zhou, Kai. |
| collection | NTU |
| description | Benefited from the advancement of modern science and technology, the wind energy has become an import source for electricity generation. However, the fluctuating nature of the wind has been a bottleneck for its widely application for years. In this report, a control strategy is designed to guarantee that a wind turbine can generate a stable output and also work in an optimal condition according to the variation of wind speed. Additionally, this design is based on neural network with Extreme Learning Machine algorithm, whose calculating speed is fast and performance is encouraging. To testify the feasibility of the design, simulation has been conducted through MATLAB and the results are show in the last part of the report. |
| first_indexed | 2024-10-01T05:18:07Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/39498 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:18:07Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/394982023-07-07T17:57:16Z Applications of extreme learning machine based neural network on wind turbine pitch angle control Zhou, Kai. Wang Youyi School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electric power::Auxiliaries, applications and electric industries Benefited from the advancement of modern science and technology, the wind energy has become an import source for electricity generation. However, the fluctuating nature of the wind has been a bottleneck for its widely application for years. In this report, a control strategy is designed to guarantee that a wind turbine can generate a stable output and also work in an optimal condition according to the variation of wind speed. Additionally, this design is based on neural network with Extreme Learning Machine algorithm, whose calculating speed is fast and performance is encouraging. To testify the feasibility of the design, simulation has been conducted through MATLAB and the results are show in the last part of the report. Bachelor of Engineering 2010-05-27T06:57:24Z 2010-05-27T06:57:24Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/39498 en Nanyang Technological University 85 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Electric power::Auxiliaries, applications and electric industries Zhou, Kai. Applications of extreme learning machine based neural network on wind turbine pitch angle control |
| title | Applications of extreme learning machine based neural network on wind turbine pitch angle control |
| title_full | Applications of extreme learning machine based neural network on wind turbine pitch angle control |
| title_fullStr | Applications of extreme learning machine based neural network on wind turbine pitch angle control |
| title_full_unstemmed | Applications of extreme learning machine based neural network on wind turbine pitch angle control |
| title_short | Applications of extreme learning machine based neural network on wind turbine pitch angle control |
| title_sort | applications of extreme learning machine based neural network on wind turbine pitch angle control |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Electric power::Auxiliaries, applications and electric industries |
| url | http://hdl.handle.net/10356/39498 |
| work_keys_str_mv | AT zhoukai applicationsofextremelearningmachinebasedneuralnetworkonwindturbinepitchanglecontrol |