Research on substation intrusion event identification method based on MTF and CNN

Abstract

This research provides a detection approach based on Markov transition field (MTF) and convolutional neural network (CNN) for substation perimeter intrusion event recognition. Because of the complexity and variety of external signals, which makes sensor detection more challenging, determining and analyzing the intrusion behavior of vibration signals induced by intrusion has become critical to improving the identification rate of intrusion-like occurrences. The obtained one-dimensional signals are mapped into two-dimensional pictures using MTF to reflect better the properties of intrusion-like signals, which can yield deeper signal details than the usual feature extraction approach. The CNN-vgg19 model’s excellent image feature identification capabilities are utilized to detect and categorize the acquired 2D feature pictures. The experimental results show that the average recognition rate of the six intrusion events is 96.7%, and the average recognition rate of the noise events is 99%, which can effectively identify the noise events and reduce the false-positive rate. The study’s findings are valuable for substation and peripheral security intrusion products.

Interpretable coal-rock cutting vibration recognition with Markov transition field and selective neural networks

“…However, in practical conditions, image signals are easily disturbed due to occlusion, and real-time performance is difficult to guarantee; optical sensing devices are expensive and require high illumination conditions. Therefore, recognition has often been accomplished in recent years through time-series signals, which have economic, real-time, and reliable advantages compared to other methods [12,13]. Wei et al [14] proposed an improved empirical variational mode decomposition (EVMD) method.…”