Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas（socalled ＂sensitive areas＂）. And yet, few studies have quantified improvements in prediction s...Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas（socalled ＂sensitive areas＂）. And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Ni n？o prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Ni n？o prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year,increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations.展开更多
算法将模型中的条件线性状态方程代入观测方程,并融合线性状态的过程噪声和观测噪声,再与非线性状态方程联立,由高斯和滤波器(Gaussian sum filter,GSF)获得非线性状态的估计;然后将估计值代入线性状态方程与观测方程,由卡尔曼滤波器(Ka...算法将模型中的条件线性状态方程代入观测方程,并融合线性状态的过程噪声和观测噪声,再与非线性状态方程联立,由高斯和滤波器(Gaussian sum filter,GSF)获得非线性状态的估计;然后将估计值代入线性状态方程与观测方程,由卡尔曼滤波器(Kalman Filter,KF)获得线性状态的估计。此外,获得的非线性状态估计的方差还用于修正线性状态的估计。将GSF-KF算法应用于目标跟踪的仿真结果表明,与现有Rao-Blackwellized粒子滤波器(Rao-Blackwellized Particle Filter,RBPF)相比,新方法在保证精度的同时,明显提高了实时性,计算时间仅约为RBPF的7%。展开更多
针对控制目标耦合导致统一电能质量调节器电能质量补偿响应速度慢、精度差、鲁棒性差等问题,提出了一种控制目标解耦的连续集模型预测(continuous control set model predictive control,CCS-MPC)直接控制策略。该控制策略依据当前时刻...针对控制目标耦合导致统一电能质量调节器电能质量补偿响应速度慢、精度差、鲁棒性差等问题,提出了一种控制目标解耦的连续集模型预测(continuous control set model predictive control,CCS-MPC)直接控制策略。该控制策略依据当前时刻被控目标电压、电流参数,构建目标唯一的预测控制模型,在简化控制器结构的同时实现串、并联侧电感电流和电容电压解耦控制,显著提高统一电能质量调节器动态性能。基于偏导数获取模型预测控制价值函数的最优解,实现参数变化、外部扰动工况下负载电压、电网电流高精度补偿。仿真验证了所提控制策略的有效性。展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19060102)the National Natural Science Foundation of China (Grant Nos. 41475101, 41690122, 41690120 and 41421005)the National Programme on Global Change and Air–Sea Interaction Interaction (Grant Nos. GASI-IPOVAI-06 and GASI-IPOVAI-01-01)
文摘Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas（socalled ＂sensitive areas＂）. And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Ni n？o prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Ni n？o prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year,increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations.
文摘针对控制目标耦合导致统一电能质量调节器电能质量补偿响应速度慢、精度差、鲁棒性差等问题,提出了一种控制目标解耦的连续集模型预测(continuous control set model predictive control,CCS-MPC)直接控制策略。该控制策略依据当前时刻被控目标电压、电流参数,构建目标唯一的预测控制模型,在简化控制器结构的同时实现串、并联侧电感电流和电容电压解耦控制,显著提高统一电能质量调节器动态性能。基于偏导数获取模型预测控制价值函数的最优解,实现参数变化、外部扰动工况下负载电压、电网电流高精度补偿。仿真验证了所提控制策略的有效性。
