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Condition Monitoring of Wind Turbine...

ProQuest Information and Learning Co.

Condition Monitoring of Wind Turbines Using Intelligent Machine Learning Techniques.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Condition Monitoring of Wind Turbines Using Intelligent Machine Learning Techniques./
作者:	Morshedizadeh, Majid.
面頁冊數:	1 online resource (107 pages)
附註:	Source: Masters Abstracts International, Volume: 56-04.
Contained By:	Masters Abstracts International56-04(E).
標題:	Engineering. -
電子資源:	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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035 $a (MiAaPQ)AAI10277990
035 $a (MiAaPQ)uwindsor:11509
035 $a AAI10277990
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Morshedizadeh, Majid. $3 1182211
245 1 0 $a Condition Monitoring of Wind Turbines Using Intelligent Machine Learning Techniques.
264 0 $c 2017
300 $a 1 online resource (107 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 56-04.
500 $a Advisers: Rupp Carriveau; David S-K Ting.
502 $a Thesis (M.S.) $c University of Windsor (Canada) $d 2017.
504 $a Includes bibliographical references
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Engineering. $3 561152
650 4 $a Energy. $3 784773
650 4 $a Mechanical engineering. $3 557493
655 7 $a Electronic books. $2 local $3 554714
690 $a 0537
690 $a 0791
690 $a 0548
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Windsor (Canada). $b MECHANICAL ENGINEERING. $3 845409
773 0 $t Masters Abstracts International $g 56-04(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10277990 $z click for full text (PQDT)

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