To facilitate rapid analysis of the oscillation stability mechanism in modular multilevel converter-based high voltage direct current(MMC-HVDC)systems and streamline the simulation process for determining MMC impedanc...To facilitate rapid analysis of the oscillation stability mechanism in modular multilevel converter-based high voltage direct current(MMC-HVDC)systems and streamline the simulation process for determining MMC impedance characteristics,a simplified mathematical simulation model for MMC closed-loop impedance is developed using the harmonic state space method.This model considers various control strategies and includes both AC-side and DC-side impedance models.By applying a Nyquist criterion-based impedance analysis method,the stability mechanisms on the AC and DC sides of the MMC are examined.In addition,a data-driven oscillation stability analysis method is also proposed,leveraging a global sensitivity algorithm based on fast model results to identify key parameters influencing MMC oscillation stability.Based on sensitivity analysis results,a parameter adjustment strategy for oscillation suppression is proposed.The simulation results from the MATLAB/Simulinkbased MMC model validate the effectiveness of the proposed method.展开更多
Target tracking using non-threshold raw data with low signal-to-noise ratio is a very difficult task, and the model uncertainty introduced by target's maneuver makes it even more challenging. In this work, a multi...Target tracking using non-threshold raw data with low signal-to-noise ratio is a very difficult task, and the model uncertainty introduced by target's maneuver makes it even more challenging. In this work, a multiple-model based method was proposed to tackle such issues. The method was developed in the framework of Bernoulli filter by integrating the model probability parameter and implemented via sequential Monte Carlo(particle) technique. Target detection was accomplished through the estimation of target's existence probability, and the estimate of target state was obtained by combining the outputs of modeldependent filtering. The simulation results show that the proposed method performs better than the TBD method implemented by the conventional multiple-model particle filter.展开更多
基金National Natural Science Foundation of China(52307127)State Key Laboratory of Power System Operation and Control(SKLD23KZ07)。
文摘To facilitate rapid analysis of the oscillation stability mechanism in modular multilevel converter-based high voltage direct current(MMC-HVDC)systems and streamline the simulation process for determining MMC impedance characteristics,a simplified mathematical simulation model for MMC closed-loop impedance is developed using the harmonic state space method.This model considers various control strategies and includes both AC-side and DC-side impedance models.By applying a Nyquist criterion-based impedance analysis method,the stability mechanisms on the AC and DC sides of the MMC are examined.In addition,a data-driven oscillation stability analysis method is also proposed,leveraging a global sensitivity algorithm based on fast model results to identify key parameters influencing MMC oscillation stability.Based on sensitivity analysis results,a parameter adjustment strategy for oscillation suppression is proposed.The simulation results from the MATLAB/Simulinkbased MMC model validate the effectiveness of the proposed method.
基金Projects(61002022,61471370)supported by the National Natural Science Foundation of China
文摘Target tracking using non-threshold raw data with low signal-to-noise ratio is a very difficult task, and the model uncertainty introduced by target's maneuver makes it even more challenging. In this work, a multiple-model based method was proposed to tackle such issues. The method was developed in the framework of Bernoulli filter by integrating the model probability parameter and implemented via sequential Monte Carlo(particle) technique. Target detection was accomplished through the estimation of target's existence probability, and the estimate of target state was obtained by combining the outputs of modeldependent filtering. The simulation results show that the proposed method performs better than the TBD method implemented by the conventional multiple-model particle filter.
