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Prediction of wind farm reactive power fast variations by adaptive one-dimensional convolutional neural network.
- Source :
-
Computers & Electrical Engineering . Dec2021:Part A, Vol. 96, pN.PAG-N.PAG. 1p. - Publication Year :
- 2021
-
Abstract
- One of the prominent problems in wind farms is voltage flicker emission. To prevent flicker emission or mitigate the impact as best as possible, a static VAr compensator (SVC) is a great candidate both economically and technically. However, SVCs cannot completely compensate the fast-changing reactive power due to delays caused by the reactive power calculation unit and the triggering fire angle of the SVC. This paper proposes a predictive control system for SVCs, by merging an additional predictive control block into the conventional control system. It is constructed based on deep neural networks, namely adaptive one-dimensional convolutional neural network (1D-CNN). The training process is conducted based on the adaptive learning weights process to enhance the prediction accuracy and training computational complexity of the 1D-CNN. Numerical results on the actual dataset in a wind farm in Manjil, Iran, have verified the forecasting accuracy and flicker mitigation of the proposed controller. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00457906
- Volume :
- 96
- Database :
- Academic Search Index
- Journal :
- Computers & Electrical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 153453135
- Full Text :
- https://doi.org/10.1016/j.compeleceng.2021.107480