Turbine system controller design based on extreme learning machine

This report emphasize on the control strategy of Wind Turbine System for maximum power extraction from the available wind. A Wind turbine model has been identified in this project with based on the parameters of a low-power rigid-drive-train SCIG-based WECS. The model has parameter with an optimum T...

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Main Author: Balamurugan Supaya
Other Authors: Wang Youyi
Format: Final Year Project
Language:English
Published: 2011
Subjects:
Online Access:http://hdl.handle.net/10356/46008
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-460082023-07-07T15:51:42Z Turbine system controller design based on extreme learning machine Balamurugan Supaya Wang Youyi School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering This report emphasize on the control strategy of Wind Turbine System for maximum power extraction from the available wind. A Wind turbine model has been identified in this project with based on the parameters of a low-power rigid-drive-train SCIG-based WECS. The model has parameter with an optimum Tip Speed ratio, λopt= 7 at wind speed of Vwind= 8 m/s, power coefficient, Cpopt= 0.476. The controller design is based on the parameterization of the system response at step changes in generator torque for a given wind speed. Using the transfer function derived from the parameterization, a Proportional - Integral - Derivative (PID) control scheme is designed. After which the PID controller is simulated in a close-loop system and the step response is observed. With the PID parameters derived, the low-power rigid-drive-train SCIG-based WECS is simulated in time domain and the Tip Speed ratio, power coefficient are observed. Bachelor of Engineering 2011-06-27T07:38:20Z 2011-06-27T07:38:20Z 2011 2011 Final Year Project (FYP) http://hdl.handle.net/10356/46008 en Nanyang Technological University 43 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
Balamurugan Supaya
Turbine system controller design based on extreme learning machine
description This report emphasize on the control strategy of Wind Turbine System for maximum power extraction from the available wind. A Wind turbine model has been identified in this project with based on the parameters of a low-power rigid-drive-train SCIG-based WECS. The model has parameter with an optimum Tip Speed ratio, λopt= 7 at wind speed of Vwind= 8 m/s, power coefficient, Cpopt= 0.476. The controller design is based on the parameterization of the system response at step changes in generator torque for a given wind speed. Using the transfer function derived from the parameterization, a Proportional - Integral - Derivative (PID) control scheme is designed. After which the PID controller is simulated in a close-loop system and the step response is observed. With the PID parameters derived, the low-power rigid-drive-train SCIG-based WECS is simulated in time domain and the Tip Speed ratio, power coefficient are observed.
author2 Wang Youyi
author_facet Wang Youyi
Balamurugan Supaya
format Final Year Project
author Balamurugan Supaya
author_sort Balamurugan Supaya
title Turbine system controller design based on extreme learning machine
title_short Turbine system controller design based on extreme learning machine
title_full Turbine system controller design based on extreme learning machine
title_fullStr Turbine system controller design based on extreme learning machine
title_full_unstemmed Turbine system controller design based on extreme learning machine
title_sort turbine system controller design based on extreme learning machine
publishDate 2011
url http://hdl.handle.net/10356/46008
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