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

Abstract

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.

Defect identification of electricity transmission line insulators based on the improved lightweight network model with computer vision assistance

“…Experimental results showed that this method could accurately identify various types of power operation links and their start and end times, with good real-time performance. The average accuracy was as high as 87.8 %, with a frame rate of 61 frames per second, which was of great significance for improving the control methods of power safety operation [ 22 ].…”