Transient stability assessment(TSA) is of great importance in power systems. For a given contingency, one of the most widely-used transient stability indices is the critical clearing time(CCT), which is a function of ...Transient stability assessment(TSA) is of great importance in power systems. For a given contingency, one of the most widely-used transient stability indices is the critical clearing time(CCT), which is a function of the pre-fault power flow.TSA can be regarded as the fitting of this function with the prefault power flow as the input and the CCT as the output. In this paper, a data-driven TSA model is proposed to estimate the CCT. The model is based on Mahalanobis-kernel regression,which employs the Mahalanobis distance in the kernel regression method to formulate a better regressor. A distance metric learning approach is developed to determine the problem-specific distance for TSA, which describes the dissimilarity between two power flow scenarios. The proposed model is more accurate compared to other data-driven methods, and its accuracy can be further improved by supplementing more training samples.Moreover, the model provides the probability density function of the CCT, and different estimations of CCT at different conservativeness levels. Test results verify the validity and the merits of the method.展开更多
在现有多种距离度量和传统谱聚类算法的基础上,提出了一种新的基于有效距离的谱聚类算法(spectral clustering based on effective distance,SCED)。SCED算法通过稀疏重构系数来构建样本与样本之间的有效距离,从而代替传统谱聚类算法中...在现有多种距离度量和传统谱聚类算法的基础上,提出了一种新的基于有效距离的谱聚类算法(spectral clustering based on effective distance,SCED)。SCED算法通过稀疏重构系数来构建样本与样本之间的有效距离,从而代替传统谱聚类算法中的欧氏距离,进行样本之间的相似度评估。与传统距离度量相比,有效距离不仅利用了样本对之间的距离信息,同时考虑了目标样本与其他所有相关样本之间的距离信息,因而该距离度量具有全局特性。在UCI标准数据集上的实验结果表明,SCED算法能有效提高聚类效果。展开更多
The continuous emergence of new targets in open scenarios leads to a substantial decrease in the performance of Inverse Synthetic Aperture Radar(ISAR)recognition systems.Also,data scarcity further exacerbates the chal...The continuous emergence of new targets in open scenarios leads to a substantial decrease in the performance of Inverse Synthetic Aperture Radar(ISAR)recognition systems.Also,data scarcity further exacerbates the challenge of identifying new classes of ISAR targets.In this paper,a few-shot incremental target recognition framework based on Scattering-Topology Properties(STPIL)is proposed.Specifically,STPIL extracts scattering-topology properties of ISAR targets as recognition features.Meanwhile,the pseudo-incremental training strategy effectively alleviates the algorithm’s forgetting of old knowledge,and improves compatibility with new classes.Besides,a feature embedding network,with few parameters,is designed based on the graph neural network.This embedding network is highly adaptable to changes in data distribution.Additionally,STPIL fully considers the joint distribution and marginal distribution in scattering features,and uses the Brownian distance metric module to make the scattering-topology features more discriminative.Experimental results on both the simulation dataset and the public measured data indicate that STPIL can effectively balance new classes with old classes,and has superior performance to other advanced methods in the incremental recognition of targets.展开更多
基金supported by National Key R&D Program of China (No.2018YFB0904500)State Grid Corporation of China。
文摘Transient stability assessment(TSA) is of great importance in power systems. For a given contingency, one of the most widely-used transient stability indices is the critical clearing time(CCT), which is a function of the pre-fault power flow.TSA can be regarded as the fitting of this function with the prefault power flow as the input and the CCT as the output. In this paper, a data-driven TSA model is proposed to estimate the CCT. The model is based on Mahalanobis-kernel regression,which employs the Mahalanobis distance in the kernel regression method to formulate a better regressor. A distance metric learning approach is developed to determine the problem-specific distance for TSA, which describes the dissimilarity between two power flow scenarios. The proposed model is more accurate compared to other data-driven methods, and its accuracy can be further improved by supplementing more training samples.Moreover, the model provides the probability density function of the CCT, and different estimations of CCT at different conservativeness levels. Test results verify the validity and the merits of the method.
文摘在现有多种距离度量和传统谱聚类算法的基础上,提出了一种新的基于有效距离的谱聚类算法(spectral clustering based on effective distance,SCED)。SCED算法通过稀疏重构系数来构建样本与样本之间的有效距离,从而代替传统谱聚类算法中的欧氏距离,进行样本之间的相似度评估。与传统距离度量相比,有效距离不仅利用了样本对之间的距离信息,同时考虑了目标样本与其他所有相关样本之间的距离信息,因而该距离度量具有全局特性。在UCI标准数据集上的实验结果表明,SCED算法能有效提高聚类效果。
文摘The continuous emergence of new targets in open scenarios leads to a substantial decrease in the performance of Inverse Synthetic Aperture Radar(ISAR)recognition systems.Also,data scarcity further exacerbates the challenge of identifying new classes of ISAR targets.In this paper,a few-shot incremental target recognition framework based on Scattering-Topology Properties(STPIL)is proposed.Specifically,STPIL extracts scattering-topology properties of ISAR targets as recognition features.Meanwhile,the pseudo-incremental training strategy effectively alleviates the algorithm’s forgetting of old knowledge,and improves compatibility with new classes.Besides,a feature embedding network,with few parameters,is designed based on the graph neural network.This embedding network is highly adaptable to changes in data distribution.Additionally,STPIL fully considers the joint distribution and marginal distribution in scattering features,and uses the Brownian distance metric module to make the scattering-topology features more discriminative.Experimental results on both the simulation dataset and the public measured data indicate that STPIL can effectively balance new classes with old classes,and has superior performance to other advanced methods in the incremental recognition of targets.
