Neural Network-Based Supplementary Frequency Controller for a DFIG Wind Farm

Ting-Hsuan Chien, Yu-Chuan Huang and Yuan-Yih Hsu
Additional contact information
Ting-Hsuan Chien: Department of Electrical Engineering, National Taiwan University, EE Building 2, No. 1, Sec. 4, Roosevelt Rd., Taipei City 106, Taiwan
Yu-Chuan Huang: Department of Electrical Engineering, National Taiwan University, EE Building 2, No. 1, Sec. 4, Roosevelt Rd., Taipei City 106, Taiwan
Yuan-Yih Hsu: Department of Electrical Engineering, National Taiwan University, EE Building 2, No. 1, Sec. 4, Roosevelt Rd., Taipei City 106, Taiwan

Energies, 2020, vol. 13, issue 20, 1-15

Abstract: An artificial neural network (ANN)-based supplementary frequency controller is designed for a doubly fed induction generator (DFIG) wind farm in a local power system. Since the optimal controller gain that gives highest the frequency nadir or lowest peak frequency is a complicated nonlinear function of load disturbance and system variables, it is not easy to use analytical methods to derive the optimal gain. The optimal gain can be reached through an exhaustive search method. However, the exhaustive search method is not suitable for online applications, since it takes a long time to perform a great number of simulations. In this work, an ANN that uses load disturbance, wind penetration, and wind speed as the inputs and the desired controller gain as the output is proposed. Once trained by a proper set of training patterns, the ANN can be employed to yield the desired gain in a very efficient manner, even when the operating condition is not included in the training set. Therefore, the proposed ANN-based controller can be used for real-time frequency control. Results from MATLAB/SIMULINK simulations performed on a local power system in Taiwan reveal that the proposed ANN can yield a better frequency response than the fixed-gain controller.

Keywords: doubly fed induction generator (DFIG); wind generation; frequency control; artificial neural network (ANN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/20/5320/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/20/5320/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:20:p:5320-:d:427082

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().