In order to realize intelligent monitoring and effective identification of operation status of gas insulated switchgear (GIS)

in this paper a kind of operation status identification method of GIS based on enhanced Gramian angular fields and residual neural network are proposed. The original vibration time-domain signals of GIS equipment are characterized by high-order through the Gramian angular fields

and it is projected into a two-dimensional picture

and then the residual neural network is used to achieve the status identification of GIS. A test simulation testing system including three typical mechanical defects of GIS is set up. The test results show that the proposed method can not only effectively characterize the status characteristics of the original signal

but also the identification accuracy is significantly improved by approximately 5% compared with the conventional method

which proves the effectiveness of the proposed method.