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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 2/2018

07.04.2015 | Original Article

Active power and reactive power dispatch of wind farm based on wavelet learning

verfasst von: Zengping Wang, Lefeng Zhang, Guohuang Li, Lina Yang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 2/2018

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Abstract

During normal operation, the doubly-fed induction generator (DFIG) generates certain range of reactive power. The DFIG based wind farm can participate in reactive power control of grid as a reactive power supply. In order to get a more stable input wind speed of the DFIG, wavelet multi-resolution analysis method is used. This paper proposes a kind of power dispatch model which considers a learning mechanism of minimum copper loss of all DFIGs in wind farm as an objective function. An active power and reactive power allocation optimization model is established. This power dispatch model makes the working condition of DFIGs and the PCC running in the optimum state. The active power and reactive power generated by wind farm satisfy the power gird requirements of both active power and reactive power. The advantage of the proposed method is verified by a case study which successfully demonstrates the learning mechanism.
Vorheriger Artikel Bat algorithm with triangle-flipping strategy for numerical optimization
Nächster Artikel Integration of fuzzy DEMATEL and FMCDM approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome

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Metadaten
Titel
Active power and reactive power dispatch of wind farm based on wavelet learning
verfasst von
Zengping Wang
Lefeng Zhang
Guohuang Li
Lina Yang
Publikationsdatum
07.04.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 2/2018
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0358-1

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