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Over the years，machine learning has made some breakthroughs in the insulation defects of gas insulated switchgear（GIS），but the traditional methods have the disadvantages of incomplete information，excessive reliance on artificial feature extraction and low diagnosis rate. In order to solve these problems，a diagnosis method based on deep graph convolutional neural network （DGCN）was proposed. Firstly，a partial discharge （PD） experimental platform was built on a 220 kV real GIS and the partial discharge signals collected by ultra high frequency sensor were converted into frequency domain spectrogram samples by Fourier transform. Then，the spectrogram samples were input into the DGCN，which undergoes graph convolution，coarsening and pooling operations to make the spectrogram structure was clearer and enrich the input information. Finally，the test samples were used to test the DGCN with set parameters. The experimental results show that the proposed method can achieve a recognition rate of 98.77% for GIS fault defects，which is significantly higher than other methods and has good robustness.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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