Gas-insulated switchgear PD signal analysis based on Hilbert-Huang transform with fractal parameters enhancement

This study proposes a novel method of partial discharge (PD) electrical signal analysis based on the Hilbert-Huang transform (HHT) with fractal feature enhancement. Firstly, this study establishes four defect types of 15 kV gas-insulated switchgear (GIS) and uses a commercial high-frequency current transformer (HFCT) to measure the electrical signals caused by the PD phenomenon. Second, the authors applied HHT for the PD electrical signal process. The HHT can represent instantaneous frequency components through empirical mode decomposition (EMD) and then transform to a 3D Hilbert energy spectrum. Finally, this study extracts the fractal parameters from the 3D energy spectrum and uses a neural network (NN) for PD recognition. To demonstrate the effectiveness of the proposed method, this study uses 160 sets of field-tested PD patterns generated by GIS, and then compares the recognition rate of the signal with and without the EMD process. The result shows that the proposed method can easily separate various defect types. The method can also be employed by the construction unit to verify the GIS quality and determine the GIS insulation status.