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Multi-scale mining of the spatio-temporal coupling relationship of dissolved gases in oil is helpful to improve the prediction accuracy of dissolved gases in oil and provide a reliable theoretical basis for transformer operation and maintenance decisions. Thereby, a multi-scale fusion prediction method for dissolved gases in transformer oil considering spatio-temporal coupling information was proposed in this study. Firstly, the Res2Net was used to extract the multi-scale time characteristics of the dissolved gas data in oil, and the periodic time feature information of the characteristic gas under different frequencies was captured. Secondly, the implicit relationship between characteristic gases was captured by calculating mutual information, the correlation between different gases was described in the form of topological graphs, and the spatial information features were extracted by using graph convolutional neural network (GCN). Finally, multi-scale temporal information and spatial information were fused and spliced, and temporal convolution network (TCN) was used to predict the dissolved gas in oil. The proposed method was validated using online oil chromatography monitoring data from a 500 kV transformer. The results show that compared with the traditional prediction method, the Res2Net-GCN-TCN model can effectively improve the prediction accuracy of dissolved gas content in oil, and the average prediction accuracy is 98.68%.
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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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