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...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
2011
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/46008 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-46008 |
---|---|
record_format |
dspace |
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 |
_version_ |
1772826421625880576 |