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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]

• Proposed intelligent neuro-genetic approaches damp out LFO in electric networks. • Standard statistical indices validate the efficacy of the proposed technique. • Results comparison confirmed the superiority of the proposed approach over others. Low-frequency oscillations should be dealt with extreme care for secure electric networks. This paper tunes the critical parameters of power system stabilizers in three different electric networks in real-time, employing the neurogenetic approach to damp out the low-frequency oscillations. The first network is a single machine infinite bus power system equipped with a power system stabilizer. Besides, the second and third networks are coordinated with second-generation flexible alternating current transmission system devices, namely a unified power flow controller and static synchronous compensator in coordination with power system stabilizers, respectively. The investigation of eigenvalue and minimum damping ratio analyses for different loading conditions proves the efficiency of the proposed approach. Additionally, the time-domain simulation comparison shows the superiority of the proposed approach over the conventional method. Besides, the satisfactory values of the statistical performance measures give confidence to the proposed approach in predicting power system stabilizer parameters and thus mitigating the low-frequency oscillations in real-time. [ABSTRACT FROM AUTHOR]