1. Prediction of wind farm reactive power fast variations by adaptive one-dimensional convolutional neural network.
- Author
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Samet, Haidar, Ketabipour, Saeedeh, Afrasiabi, Shahabodin, Afrasiabi, Mousa, and Mohammadi, Mohammad
- Subjects
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CONVOLUTIONAL neural networks , *REACTIVE power , *STATIC VAR compensators , *SIGNAL convolution , *PREDICTIVE control systems , *FARM mechanization , *DEEP learning , *WIND power plants - 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]
- Published
- 2021
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