Identification of Key Links in Electric Power Operation Based-Spatiotemporal Mixing Convolution Neural Network

As the scale of the power system continues to expand,the environment for power operations becomes more and more complex.Existing risk management and control methods for power operations can only set the same risk detection standard and conduct the risk detection for any scenario indiscriminately.Therefore,more reliable and accurate security control methods are urgently needed.In order to improve the accuracy and reliability of the operation risk management and control method,this paper proposes a method for identifying the key links in the whole process of electric power operation based on the spatiotemporal hybrid convolutional neural network.To provide early warning and control of targeted risks,first,the video stream is framed adaptively according to the pixel changes in the video stream.Then,the optimized MobileNet is used to extract the feature map of the video stream,which contains both time-series and static spatial scene information.The feature maps are combined and non-linearly mapped to realize the identification of dynamic operating scenes.Finally,training samples and test samples are produced by using the whole process image of a power company in Xinjiang as a case study,and the proposed algorithm is compared with the unimproved MobileNet.The experimental results demonstrated that the method proposed in this paper can accurately identify the type and start and end time of each operation link in the whole process of electric power operation,and has good real-time performance.The average accuracy of the algorithm can reach 87.8%,and the frame rate is 61 frames/s,which is of great significance for improving the reliability and accuracy of security control methods.

Evaluating the Vulnerability of Integrated Electricity-heat-gas Systems Based on the High-dimensional Random Matrix Theory

Faced with the tight coupling of multi energy sources,the interaction between different energy supply systems makes it difficult for integrated energy systems(IES)to identify weak nodes.Based on the analysis of the data generated by the actual operation of IES,this paper proposes a weak node identification method based on random matrix theory(RMT).First,establish a unified power flow model for IES.Secondly.introduce RMT and the characteristics of weak nodes,without considering the detailed physical model of the system,using historical data and real-time data to construct the random matrix.Thirdly,the two limit spectrum distribution functions(Marchenko-Pastur law and ring law)are used to qualitatively analyze the system’s operating status,calculate linear eigenvalue statistics such as mean spectral radius(MSR),and establish the weak node identification model based on entropy theory.Finally,the simulation of IES verifies the effectiveness of the proposed method and provides a new approach for the identification of weak nodes in IES.

介于二维和三维晶体间的超薄晶态材料的X射线衍射及对称性研究

为了深入理解超薄晶态材料的结构和对称性的独特性,本文在充分考虑超薄晶态材料堆积自由度的情况下,从数学和晶体学角度对介于二维与三维之间的超薄纳米晶体的对称性和X射线衍射进行了探讨.认为超薄纳米晶体的对称性需要用数学层群(layer group)来描述;另外,由于其层堆叠方向上不具有周期性结构,超薄纳米晶体可能产生特异衍射特征.为了验证这一观点,研究了三类在层堆叠方向不具有周期性的典型超薄纳米晶体的X射线衍射,从实验上观察到了超薄纳米晶体与其体相三维晶体粉末X射线衍射的不同;针对基于三维晶体的X射线衍射模拟软件不再适用于超薄纳米晶体的情况,发展了超薄纳米晶体的全结构X射线衍射分析方法,很好地模拟了超薄纳米晶粉末X射线衍射图.该研究对超薄纳米晶态材料的结构及对称性提出了新见解,将有益于推动相关材料的构效关系研究和新功能开发.