Micro-satellite cluster enables a whole new class of missions for communications, remote sensing, and scientific research for both civilian and military purposes. Synchronizing the time of the satellites in a cluster ...Micro-satellite cluster enables a whole new class of missions for communications, remote sensing, and scientific research for both civilian and military purposes. Synchronizing the time of the satellites in a cluster is important for both cluster sensing capabilities and its autonomous operating. However, the existing time synchronization methods are not suitable for microsatellite cluster, because it requires too many human interventions and occupies too much ground control resource. Although, data post-process may realize the equivalent time synchronization, it requires processing time and powerful computing ability on the ground, which cannot be implemented by cluster itself. In order to autonomously establish and maintain the time benchmark in a cluster, we propose a compact time difference compensation system（TDCS）, which is a kind of time control loop that dynamically adjusts the satellite reference frequency according to the time difference. Consequently, the time synchronization in the cluster can be autonomously achieved on-orbit by synchronizing the clock of other satellites to a chosen one＇s. The experimental result shows that the standard deviation of time synchronization is about 102 ps when the carrier to noise ratio（CNR） is 95 d BHz, and the standard deviation of corresponding frequency difference is approximately0.36 Hz.展开更多
高渗透率新能源波动下系统动态频率预测是实现受端网络频率安全态势感知的基础。该文提出一种基于混合量测和物理状态方程联合驱动的新能源电力系统双向树状长短期记忆网络(combined equation-of-state-driven and data-driven bi-direc...高渗透率新能源波动下系统动态频率预测是实现受端网络频率安全态势感知的基础。该文提出一种基于混合量测和物理状态方程联合驱动的新能源电力系统双向树状长短期记忆网络(combined equation-of-state-driven and data-driven bi-directional tree-struct long short term memory,CEOSD-BITREE-LSTM)动态频率预测方法。首先,引入双层多头注意力图神经网络,提出考虑同步相量测量单元(synchronous phasor measurement unit,PMU)和数据采集与监视控制系统装置(supervisory control and data acquisition,SCADA)量测差异性和时序同步性的混合量测融合策略;其次,依据PMU密集采样特性,建立计及源网荷物理联系的线性时变状态方程,刻画物理-数据空间的频率特征交互关系;然后,考虑新能源出力、负荷波动等不确定因素,结合以PMU并行搜索调频资源形成的拓扑结构,构建CEOSD-BITREE-LSTM动态频率预测模型,实现系统频率态势的高精度预测。最后,以改进新英格兰10机39节点、三区互联系统为算例,验证该文所提方法的可行性和有效性。展开更多
基金supported by the National Natural Science Foundation of China(61401389)the Joint Fund of the Ministry of Education of China(6141A02033310)
文摘Micro-satellite cluster enables a whole new class of missions for communications, remote sensing, and scientific research for both civilian and military purposes. Synchronizing the time of the satellites in a cluster is important for both cluster sensing capabilities and its autonomous operating. However, the existing time synchronization methods are not suitable for microsatellite cluster, because it requires too many human interventions and occupies too much ground control resource. Although, data post-process may realize the equivalent time synchronization, it requires processing time and powerful computing ability on the ground, which cannot be implemented by cluster itself. In order to autonomously establish and maintain the time benchmark in a cluster, we propose a compact time difference compensation system（TDCS）, which is a kind of time control loop that dynamically adjusts the satellite reference frequency according to the time difference. Consequently, the time synchronization in the cluster can be autonomously achieved on-orbit by synchronizing the clock of other satellites to a chosen one＇s. The experimental result shows that the standard deviation of time synchronization is about 102 ps when the carrier to noise ratio（CNR） is 95 d BHz, and the standard deviation of corresponding frequency difference is approximately0.36 Hz.
文摘高渗透率新能源波动下系统动态频率预测是实现受端网络频率安全态势感知的基础。该文提出一种基于混合量测和物理状态方程联合驱动的新能源电力系统双向树状长短期记忆网络(combined equation-of-state-driven and data-driven bi-directional tree-struct long short term memory,CEOSD-BITREE-LSTM)动态频率预测方法。首先,引入双层多头注意力图神经网络,提出考虑同步相量测量单元(synchronous phasor measurement unit,PMU)和数据采集与监视控制系统装置(supervisory control and data acquisition,SCADA)量测差异性和时序同步性的混合量测融合策略;其次,依据PMU密集采样特性,建立计及源网荷物理联系的线性时变状态方程,刻画物理-数据空间的频率特征交互关系;然后,考虑新能源出力、负荷波动等不确定因素,结合以PMU并行搜索调频资源形成的拓扑结构,构建CEOSD-BITREE-LSTM动态频率预测模型,实现系统频率态势的高精度预测。最后,以改进新英格兰10机39节点、三区互联系统为算例,验证该文所提方法的可行性和有效性。
