In this study, we address the challenge of noise reduction in environments characterized by impulsive noise and missing input data in active noise control (ANC) systems, where existing algorithms often fail to deliver satisfactory results. Background noise, especially impulsive noise, poses a significant obstacle to signal processing and noise reduction. Furthermore, data loss during transmission or acquisition further complicates the noise reduction task. In this paper, a filtered-x imputation of the missing data maximum correntropy criterion (FxImdMCC) algorithm is proposed based on an imputation model, least mean square, and the maximum correntropy criterion (MCC), which can effectively reduce the impact of outliers and missing input data. The simulation results demonstrate the efficacy of the proposed FxImdMCC algorithm, which significantly outperforms existing algorithms in the context of active impulsive noise control.

As the penetration rate of new energy continues to increase, the active voltage support capability of the power system is decreasing. The grid-forming static var generator (GFM-SVG) features the advantages of fast dynamic response, strong reactive power support, and high overload capacity, which play an important role in maintaining voltage stability. However, the parameters of the GFM-SVG are often unknown due to trade secret reasons. Meanwhile, the parameters may be changed during the long-term operation of the system, which brings challenges to the system stability analysis and control. Aiming at this problem, a parameter identification method based on trajectory sensitivity analysis and the proximal policy optimization (PPO) algorithm is proposed in this paper. Firstly, through trajectory sensitivity analysis, the key influential parameters on the output characteristics of the GFM-SVG can be selected, which can reduce the dimensionality of the identification parameters and improve the identification efficiency. Then, a parameter identification framework based on the PPO algorithm is constructed for GFM-SVGs, which utilizes its adaptive learning capability to achieve accurate identification of the key parameters of the system. Finally, the effectiveness of the proposed parameter identification method is verified through simulation examples. The simulation results show that the identification error of the parameters in the GFM-SVG is small. The proposed method can characterize the output response of the GFM-SVG under different operating conditions.