Boosting is one of the most representational ensemble prediction methods. It can be divided into two se-ries: Boost-by-majority and Adaboost. This paper briefly introduces the research status of Boosting and one of it...Boosting is one of the most representational ensemble prediction methods. It can be divided into two se-ries: Boost-by-majority and Adaboost. This paper briefly introduces the research status of Boosting and one of its seri-als-AdaBoost,analyzes the typical algorithms of AdaBoost.展开更多
针对具有非最小相位特性的单电感双输出Buck-Boost变换器(SIDO Buck-Boost)输出两支路存在严重的交叉影响、控制困难以及系统暂态性能差等问题,提出一种基于扩张状态观测器(extended state observer,ESO)的主路微分平坦控制(differentia...针对具有非最小相位特性的单电感双输出Buck-Boost变换器(SIDO Buck-Boost)输出两支路存在严重的交叉影响、控制困难以及系统暂态性能差等问题,提出一种基于扩张状态观测器(extended state observer,ESO)的主路微分平坦控制(differential flatness based control,DFBC)和支路改进双闭环自抗扰控制(active disturbance rejection controller,ADRC)的控制策略.首先,根据主路微分平坦理论,在主路控制中设计微分平坦控制器,并对微分平坦系统进行误差反馈;设计ESO对主路的扰动项进行观测,将观测后的状态量反馈到微分平坦控制器中.其次,针对支路存在耦合以及右半平面零点的问题,设计改进型双闭环ADRC进行系统解耦,其中,电流内环选取基于模型补偿和前馈补偿的ADRC,电压外环选取普通ADRC,然后,利用Lyapunov理论证明系统的稳定性.最后,在Matlab/Simulink平台中搭建了仿真模型,并基于HIL搭建了实验平台.仿真及实验结果表明:所提控制策略减小了输出两支路之间的交叉影响,解决了非最小相位系统控制困难的问题,提高了系统的暂态响应性能.展开更多
The prediction of power grid faults based on meteorological factors is of great significance to reduce economic losses caused by power grid faults. However, the existing methods fail to effectively extract key feature...The prediction of power grid faults based on meteorological factors is of great significance to reduce economic losses caused by power grid faults. However, the existing methods fail to effectively extract key features and accurately predict fault types due to the complexity of meteorological factors and their nonlinear relationships. In response to these challenges, we propose the Feature-Enhanced XGBoost power grid fault prediction method (FE-XGBoost). Specifically, we first combine the gradient boosting decision tree and recursive feature elimination method to extract essential features from meteorological data. Then, we incorporate a piecewise linear chaotic map to enhance the optimization accuracy of the sparrow search algorithm. Finally, we construct an XGBoost-based model for the classification prediction of power grid meteorological faults and optimize the hyperparameters such as the optimal tree depth, optimal learning rate, and optimal number of iterations using an enhanced sparrow search algorithm. Experimental results demonstrate that our method outperforms the baseline models in predicting power grid faults accurately.展开更多
文摘Boosting is one of the most representational ensemble prediction methods. It can be divided into two se-ries: Boost-by-majority and Adaboost. This paper briefly introduces the research status of Boosting and one of its seri-als-AdaBoost,analyzes the typical algorithms of AdaBoost.
文摘针对具有非最小相位特性的单电感双输出Buck-Boost变换器(SIDO Buck-Boost)输出两支路存在严重的交叉影响、控制困难以及系统暂态性能差等问题,提出一种基于扩张状态观测器(extended state observer,ESO)的主路微分平坦控制(differential flatness based control,DFBC)和支路改进双闭环自抗扰控制(active disturbance rejection controller,ADRC)的控制策略.首先,根据主路微分平坦理论,在主路控制中设计微分平坦控制器,并对微分平坦系统进行误差反馈;设计ESO对主路的扰动项进行观测,将观测后的状态量反馈到微分平坦控制器中.其次,针对支路存在耦合以及右半平面零点的问题,设计改进型双闭环ADRC进行系统解耦,其中,电流内环选取基于模型补偿和前馈补偿的ADRC,电压外环选取普通ADRC,然后,利用Lyapunov理论证明系统的稳定性.最后,在Matlab/Simulink平台中搭建了仿真模型,并基于HIL搭建了实验平台.仿真及实验结果表明:所提控制策略减小了输出两支路之间的交叉影响,解决了非最小相位系统控制困难的问题,提高了系统的暂态响应性能.
基金supported by the Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.(Research on Power Meteorology Digitalization Application for Future Climate Scenarios and New Energy Operation Risks,J2023076).
文摘The prediction of power grid faults based on meteorological factors is of great significance to reduce economic losses caused by power grid faults. However, the existing methods fail to effectively extract key features and accurately predict fault types due to the complexity of meteorological factors and their nonlinear relationships. In response to these challenges, we propose the Feature-Enhanced XGBoost power grid fault prediction method (FE-XGBoost). Specifically, we first combine the gradient boosting decision tree and recursive feature elimination method to extract essential features from meteorological data. Then, we incorporate a piecewise linear chaotic map to enhance the optimization accuracy of the sparrow search algorithm. Finally, we construct an XGBoost-based model for the classification prediction of power grid meteorological faults and optimize the hyperparameters such as the optimal tree depth, optimal learning rate, and optimal number of iterations using an enhanced sparrow search algorithm. Experimental results demonstrate that our method outperforms the baseline models in predicting power grid faults accurately.
