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
目的本研究旨在构建一个基于临床和影像学特征的极端梯度提升(extreme gradient boosting,XGBoost)模型,以鉴别乳腺非肿块病变的良恶性。材料与方法收集2018年1月至2024年7月2个机构,2种乳腺X线设备检查的有病理结果的首诊乳腺非肿块病...目的本研究旨在构建一个基于临床和影像学特征的极端梯度提升(extreme gradient boosting,XGBoost)模型,以鉴别乳腺非肿块病变的良恶性。材料与方法收集2018年1月至2024年7月2个机构,2种乳腺X线设备检查的有病理结果的首诊乳腺非肿块病变480个。患者被分为建模组[n=310,数字乳腺X线摄影(digital mammography,DM)检查]、内部验证组(n=108,DM检查),和外部验证组[n=62,数字乳腺体层合成摄影(digital breast tomosynthesis,DBT)检查]。记录患者术前乳腺X线(DM或DBT),MRI以及临床特征。采用XGBoost算法和多因素逻辑回归分析,分别构建XGBoost模型和逻辑回归(logistic regression,LR)模型。使用受试者工作特征(receiver operating characteristic,ROC)曲线评估模型的诊断效能。结果在建模组中,患者以7∶3随机分为训练集(n=217)和测试集(n=93)。训练集、测试集、训练集的内部验证组及训练集的外部验证组中,恶性非肿块病灶分别为159(73%)、58(62%)、73(68%)和43(69%)。XGBoost模型的诊断效能明显优于LR模型,在独立的训练集、测试集、训练集的内部验证组及训练集的外部验证组中均表现出良好的诊断效能,曲线下面积(area under the curve,AUC)在0.884~0.913之间。XGBoost模型在四个队列中也表现出良好的校准能力和临床净获益。结论XGBoost模型能够准确鉴别乳腺非肿块病变的良恶性,具有推广应用的潜力。展开更多
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.
文摘目的本研究旨在构建一个基于临床和影像学特征的极端梯度提升(extreme gradient boosting,XGBoost)模型,以鉴别乳腺非肿块病变的良恶性。材料与方法收集2018年1月至2024年7月2个机构,2种乳腺X线设备检查的有病理结果的首诊乳腺非肿块病变480个。患者被分为建模组[n=310,数字乳腺X线摄影(digital mammography,DM)检查]、内部验证组(n=108,DM检查),和外部验证组[n=62,数字乳腺体层合成摄影(digital breast tomosynthesis,DBT)检查]。记录患者术前乳腺X线(DM或DBT),MRI以及临床特征。采用XGBoost算法和多因素逻辑回归分析,分别构建XGBoost模型和逻辑回归(logistic regression,LR)模型。使用受试者工作特征(receiver operating characteristic,ROC)曲线评估模型的诊断效能。结果在建模组中,患者以7∶3随机分为训练集(n=217)和测试集(n=93)。训练集、测试集、训练集的内部验证组及训练集的外部验证组中,恶性非肿块病灶分别为159(73%)、58(62%)、73(68%)和43(69%)。XGBoost模型的诊断效能明显优于LR模型,在独立的训练集、测试集、训练集的内部验证组及训练集的外部验证组中均表现出良好的诊断效能,曲线下面积(area under the curve,AUC)在0.884~0.913之间。XGBoost模型在四个队列中也表现出良好的校准能力和临床净获益。结论XGBoost模型能够准确鉴别乳腺非肿块病变的良恶性,具有推广应用的潜力。
