The data-driven transient stability assessment(TSA)of power systems can predict online real-time prediction by learning the temporal features before and after faults.However,the accuracy of the assessment is limited b...The data-driven transient stability assessment(TSA)of power systems can predict online real-time prediction by learning the temporal features before and after faults.However,the accuracy of the assessment is limited by the quality of the data and has weak transferability.Based on this,this paper proposes a method for TSA of power systems based on an improved extreme gradient boosting(XGBoost)model.Firstly,the gradient detection method is employed to remove noise interference while maintaining the original time series trend.On this basis,a focal loss function is introduced to guide the training of theXGBoostmodel,enhancing the deep exploration of minority class samples to improve the accuracy of the model evaluation.Furthermore,to improve the generalization ability of the evaluation model,a transfer learning method based on model parameters and sample augmentation is proposed.The simulation analysis on the IEEE 39-bus system demonstrates that the proposed method,compared to the traditional machine learning-based transient stability assessment approach,achieves an average improvement of 2.16%in evaluation accuracy.Specifically,under scenarios involving changes in topology structure and operating conditions,the accuracy is enhanced by 3.65%and 3.11%,respectively.Moreover,the model updating efficiency is enhanced by 14–15 times,indicating the model’s transferable and adaptive capabilities across multiple scenarios.展开更多
We investigate the linear momentum density of light, which can be decomposed into spin and orbital parts, in the complex three-dimensional field distributions of tightly focused vortex segmented beams. The chosen angu...We investigate the linear momentum density of light, which can be decomposed into spin and orbital parts, in the complex three-dimensional field distributions of tightly focused vortex segmented beams. The chosen angular spectrum exhibits two spatially separated vortices of opposite charge and orthogonal circular polarization to generate phase vortices in a meridional plane of observation. In the vicinity of those vortices, regions of negative orbital linear momentum occur. Besides these phase vortices, the occurrence of transverse orbital angular momentum manifests in a vortex charge-dependent relative shift of the energy density and linear momentum density.展开更多
基金This work is supported by the State Grid Shanxi Electric Power Company Technology Project(52053023000B).
文摘The data-driven transient stability assessment(TSA)of power systems can predict online real-time prediction by learning the temporal features before and after faults.However,the accuracy of the assessment is limited by the quality of the data and has weak transferability.Based on this,this paper proposes a method for TSA of power systems based on an improved extreme gradient boosting(XGBoost)model.Firstly,the gradient detection method is employed to remove noise interference while maintaining the original time series trend.On this basis,a focal loss function is introduced to guide the training of theXGBoostmodel,enhancing the deep exploration of minority class samples to improve the accuracy of the model evaluation.Furthermore,to improve the generalization ability of the evaluation model,a transfer learning method based on model parameters and sample augmentation is proposed.The simulation analysis on the IEEE 39-bus system demonstrates that the proposed method,compared to the traditional machine learning-based transient stability assessment approach,achieves an average improvement of 2.16%in evaluation accuracy.Specifically,under scenarios involving changes in topology structure and operating conditions,the accuracy is enhanced by 3.65%and 3.11%,respectively.Moreover,the model updating efficiency is enhanced by 14–15 times,indicating the model’s transferable and adaptive capabilities across multiple scenarios.
文摘We investigate the linear momentum density of light, which can be decomposed into spin and orbital parts, in the complex three-dimensional field distributions of tightly focused vortex segmented beams. The chosen angular spectrum exhibits two spatially separated vortices of opposite charge and orthogonal circular polarization to generate phase vortices in a meridional plane of observation. In the vicinity of those vortices, regions of negative orbital linear momentum occur. Besides these phase vortices, the occurrence of transverse orbital angular momentum manifests in a vortex charge-dependent relative shift of the energy density and linear momentum density.
