Deep Learning Forecaster-Based Controller for SVC: Wind Farm Flicker Mitigation

The main aim in this paper is to develop a method based on deep learning, namely convolutional neural network (CNN), to directly learn non-stationary and complex features from raw reactive power of a wind farm time series and contribute a predictive controller to mitigate voltage flicker through a SVC connected to a wind farm in parallel manner. Besides, a time-variant current source model to characterize a power source in which its amplitude and phase change about every 0.01s. The actual recorded data of a wind farm in Manjil, Iran is used as the input dataset to model a wind farm and feed real-time predictive controller based on CNN of the wind farm. Numerical results in terms of flicker sensation and short-term flicker perceptibility (Pst) measurement are used to verify the performance of the proposed method through comparison with wind farm performance without SVC and SVC with a common control system.