國立虎尾科技大學 |

On-Line Parameter Identification of Doubly-Fed Induction Generators from Measurement Data.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	On-Line Parameter Identification of Doubly-Fed Induction Generators from Measurement Data./
作者:	Guo, Shaotong.
面頁冊數:	1 online resource (107 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
Contained By:	Dissertation Abstracts International78-12B(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355094459

On-Line Parameter Identification of Doubly-Fed Induction Generators from Measurement Data.
Guo, Shaotong.

On-Line Parameter Identification of Doubly-Fed Induction Generators from Measurement Data. - 1 online resource (107 pages)

Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Doubly-Fed Induction Generators (DFIG) are widely used nowadays for renewable energy generation. Control techniques need to be developed to match the growing DFIG wind turbine sizes and capacity. This requires better modeling verification tools of the DFIG which also bring other benefits. They can help to better increase their reliability, as well as improving the efficiency of the power output. This research mainly demonstrates the verification of DFIG electric models by identifying the parameters of operating DFIGs. The 4th order and 2nd order DFIG electric models were derived and specifically used for the parameter identification process. In order to provide the measurement input data for the parameter identification algorithm, a customized DFIG simulation model was built in MATLAB SIMULINK. By applying the Model Reference Adaptive Control (MRAC) algorithm, the electric parameters of the DFIG models have been identified with high accuracy and short convergence time. The proposed MRAC algorithm also performs well on identifying the parameters when the rotational speed changes. In the field, the measurement data can be taken from Phasor Measurement Units (PMU) to achieve the on-line identification. This identification technique can be used as an ad-hoc function block as well, that can cooperate with other DFIG control techniques, as well as provide reference information indicating the real-time operation condition of the wind- turbines.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355094459Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

On-Line Parameter Identification of Doubly-Fed Induction Generators from Measurement Data.
LDR:02756ntm a2200349Ki 4500 001 910729
005 20180517112608.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355094459
035 $a (MiAaPQ)AAI10264001
035 $a (MiAaPQ)wsu:12014
035 $a AAI10264001
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Guo, Shaotong. $3 1182169
245 1 0 $a On-Line Parameter Identification of Doubly-Fed Induction Generators from Measurement Data.
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: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
500 $a Adviser: Anjan Bose.
502 $a Thesis (Ph.D.) $c Washington State University $d 2017.
504 $a Includes bibliographical references
520 $a Doubly-Fed Induction Generators (DFIG) are widely used nowadays for renewable energy generation. Control techniques need to be developed to match the growing DFIG wind turbine sizes and capacity. This requires better modeling verification tools of the DFIG which also bring other benefits. They can help to better increase their reliability, as well as improving the efficiency of the power output. This research mainly demonstrates the verification of DFIG electric models by identifying the parameters of operating DFIGs. The 4th order and 2nd order DFIG electric models were derived and specifically used for the parameter identification process. In order to provide the measurement input data for the parameter identification algorithm, a customized DFIG simulation model was built in MATLAB SIMULINK. By applying the Model Reference Adaptive Control (MRAC) algorithm, the electric parameters of the DFIG models have been identified with high accuracy and short convergence time. The proposed MRAC algorithm also performs well on identifying the parameters when the rotational speed changes. In the field, the measurement data can be taken from Phasor Measurement Units (PMU) to achieve the on-line identification. This identification technique can be used as an ad-hoc function block as well, that can cooperate with other DFIG control techniques, as well as provide reference information indicating the real-time operation condition of the wind- turbines.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
650 4 $a Engineering. $3 561152
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
690 $a 0537
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Washington State University. $b Electrical Engineering. $3 1182170
773 0 $t Dissertation Abstracts International $g 78-12B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10264001 $z click for full text (PQDT)