In this study the principal component analysis (PCA) and geographically weighted regression (GWR) are combined to estimate the spatial distribution of water requirement of the winter wheat in North China while the eff...In this study the principal component analysis (PCA) and geographically weighted regression (GWR) are combined to estimate the spatial distribution of water requirement of the winter wheat in North China while the effect of the macroand micro-topographic as well as the meteorological factors on the crop water requirement is taking into account. The spatial distribution characteristic of the water requirement of the winter wheat in North China and its formation are analyzed based on the spatial variation of the main affecting factors and the regression coefficients. The findings reveal that the collinearity can be effectively removed when PCA is applied to process all of the affecting factors. The regression coefficients of GWR displayed a strong variability in space, which can better explain the spatial differences of the effect of the affecting factors on the crop water requirement. The evaluation index of the proposed method in this study is more efficient than the widely used Kriging method. Besides, it could clearly show the effect of those affecting factors in different spatial locations on the crop water requirement and provide more detailed information on the region where those factors suddenly change. To sum up, it is of great reference significance for the estimation of the regional crop water requirement.展开更多
高比例风电接入加剧了风电机组与同步机间的动态耦合,导致多机广域阻尼控制器(wide area damping controllers,WADC)难以协同整定。在复杂工况下,多机WADC难以有效抑制振荡,甚至恶化系统阻尼。为解决上述问题,提出一种面向含风电电力系...高比例风电接入加剧了风电机组与同步机间的动态耦合,导致多机广域阻尼控制器(wide area damping controllers,WADC)难以协同整定。在复杂工况下,多机WADC难以有效抑制振荡,甚至恶化系统阻尼。为解决上述问题,提出一种面向含风电电力系统的智能广域阻尼控制策略。首先,建立含双馈风机的多机广域阻尼协同控制模型,并基于联合测度指标选择广域阻尼控制回路。然后,构建融合主成分分析(principal component analysis,PCA)与多智能体深度确定性策略梯度(multi agent deep deterministic policy gradient,MADDPG)的控制框架,实现高维状态空间下的参数协同优化。最后,在改进的两区域四机系统及我国西北风火打捆送端系统开展仿真分析,结果表明该策略能够显著提升区域间低频振荡模态的阻尼水平,在复杂工况下依然具备良好的振荡抑制效果和工程适应性。展开更多
基金supported by the National Basic Research Program of China (2006CB403406)the National Natural Science Foundation of China(51079154)the National HighTech Research & Development Program of China (2011AA100502)
文摘In this study the principal component analysis (PCA) and geographically weighted regression (GWR) are combined to estimate the spatial distribution of water requirement of the winter wheat in North China while the effect of the macroand micro-topographic as well as the meteorological factors on the crop water requirement is taking into account. The spatial distribution characteristic of the water requirement of the winter wheat in North China and its formation are analyzed based on the spatial variation of the main affecting factors and the regression coefficients. The findings reveal that the collinearity can be effectively removed when PCA is applied to process all of the affecting factors. The regression coefficients of GWR displayed a strong variability in space, which can better explain the spatial differences of the effect of the affecting factors on the crop water requirement. The evaluation index of the proposed method in this study is more efficient than the widely used Kriging method. Besides, it could clearly show the effect of those affecting factors in different spatial locations on the crop water requirement and provide more detailed information on the region where those factors suddenly change. To sum up, it is of great reference significance for the estimation of the regional crop water requirement.
