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Condition Monitoring of Wind Turbine...
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ProQuest Information and Learning Co.
Condition Monitoring of Wind Turbines Using Intelligent Machine Learning Techniques.
Record Type:
Language materials, manuscript : Monograph/item
Title/Author:
Condition Monitoring of Wind Turbines Using Intelligent Machine Learning Techniques./
Author:
Morshedizadeh, Majid.
Description:
1 online resource (107 pages)
Notes:
Source: Masters Abstracts International, Volume: 56-04.
Contained By:
Masters Abstracts International56-04(E).
Subject:
Engineering. -
Online resource:
click for full text (PQDT)
ISBN:
9781369860245
Condition Monitoring of Wind Turbines Using Intelligent Machine Learning Techniques.
Morshedizadeh, Majid.
Condition Monitoring of Wind Turbines Using Intelligent Machine Learning Techniques.
- 1 online resource (107 pages)
Source: Masters Abstracts International, Volume: 56-04.
Thesis (M.S.)
Includes bibliographical references
Wind Turbine condition monitoring can detect anomalies in turbine performance which have the potential to result in unexpected failure and financial loss. This study examines common Supervisory Control And Data Acquisition (SCADA) data over a period of 20 months for 21 pitch regulated 2.3 MW turbines and is presented in three manuscripts.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9781369860245Subjects--Topical Terms:
561152
Engineering.
Index Terms--Genre/Form:
554714
Electronic books.
Condition Monitoring of Wind Turbines Using Intelligent Machine Learning Techniques.
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Condition Monitoring of Wind Turbines Using Intelligent Machine Learning Techniques.
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Advisers: Rupp Carriveau; David S-K Ting.
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University of Windsor (Canada)
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2017.
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Includes bibliographical references
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Wind Turbine condition monitoring can detect anomalies in turbine performance which have the potential to result in unexpected failure and financial loss. This study examines common Supervisory Control And Data Acquisition (SCADA) data over a period of 20 months for 21 pitch regulated 2.3 MW turbines and is presented in three manuscripts.
520
$a
First, power curve monitoring is targeted applying various types of Artificial Neural Networks to increase modeling accuracy. It is shown how the proposed method can significantly improve network reliability compared with existing models.
520
$a
Then, an advance technique is utilized to create a smoother dataset for network training followed by establishing dynamic ANFIS network. At this stage, designed network aims to predict power generation in future hours.
520
$a
Finally, a recursive principal component analysis is performed to extract significant features to be used as input parameters of the network. A novel fusion technique is then employed to build an advanced model to make predictions of turbines performance with favorably low errors.
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Ann Arbor, Mich. :
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ProQuest,
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2018
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Mode of access: World Wide Web
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Engineering.
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ProQuest Information and Learning Co.
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University of Windsor (Canada).
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click for full text (PQDT)
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