The invention discloses a GIS disconnecting switch multi-state intelligent
sensing system, and relates to the field of GIS disconnecting switch state monitoring. A self-calibration multi-mode sensor is deployed for multi-
source data acquisition, and novel sensors including terahertz imaging and the like are included; in data preprocessing,
deep learning noise reduction is applied,
fuzzy entropy is used for dynamic weighted fusion, and abnormal values are processed by an
improved algorithm;
feature extraction is combined with a plurality of frontier algorithms to process vibration signals, and CRNN is used to analyze acoustic signals; the QPSO evidence theory is adopted for state fusion
perception, the node relation is learned by means of GNN, and the weight is adjusted according to the
information gain rate; state assessment and early warning are based on transfer learning, GAN and LSTM-attention mechanisms, and early warning priorities are ranked by FAHP. According to the invention, multi-mode accurate acquisition, intelligent
data processing, deep
feature mining, innovative fusion
perception, accurate evaluation and early warning and efficient fault diagnosis and positioning are realized, the state of the GIS isolation switch can be comprehensively and accurately perceived, the
system is self-learned and optimized, the
equipment safety is guaranteed, the risk of a power
system is reduced, and the power operation and maintenance benefits are improved.