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.

Skeleton-based Violation Action Recognition Method for Safety Supervision in Operation Field of Distribution Network Based on Graph Convolutional Network

Safety accidents in the operation field of the distribution network often occur,which seriously endanger the safety and lives of operators.Existing identification methods for safety risk can identify static safety risks,such as no-helmet,no-safety gloves,etc.,but fail to identify risks in the dynamic actions of operators.Therefore,this paper proposes a skeletonbased violation action-recognition method for supervision of safety during operations in a distribution network,i.e.,based on spatial temporal graph convolutional network(STGCN)and key joint attention module(KJAM),which can implement dynamic violation behavior recognition of operators.In this method,the human posture estimation method,i.e.Multi-Person Pose Estimation,is utilized to extract the skeleton information of operators during operations,and to construct an undirected graph,which reflects the movement and posture of the human body.Then,the STGCN is utilized to identify actions of operators that can lead to dynamic violations.In addition,the KJAM captures important joint information of operators.The effectiveness and superiority of the proposed method are verified in comparison to other action recognition methods.The experimental results show that the proposed method has higher recognition accuracy for common violations collected at the actual operation site of the distribution network and shows a strong generalization ability,which can be applied to the video monitoring system of field operations to reduce the occurrence of safety accidents.

State Evaluation Based on Feature Identification of Measurement Data:for Resilient Power System

Resilient power systems urgently need real-time evaluation of their operational states.By mining the characteristics of the grid operational data and mapping them to the operational state,this paper proposes a method to evaluate the real-time state and the evolution direction of power systems.First,the state evaluation matrix is constructed using nodal voltages.Then,from the data-driven perspective,the grid state is embodied in the operational data change.Furthermore,four indicators are proposed to characterize the power grid statefrom inherent physical and operating characteristics perspectives.Finally,through the simulations of a real power grid in China,it is shown that the method proposed in this paper can adequately characterize the power grid state,and is robust against bad data.