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To solve the problem of low recognition accuracy of transformer insulation oil gas fault diagnosis, the slime mold algorithm (SMA) is improved by the reverse learning strategy to form the improved slime mold algorithm (ISMA), thus to improve the global optimization ability and optimize the support vector machine (SVM). An ISMA-SVM optimized fault diagnosis model is established, and the sample set is used for learning and training. The diagnosis and recoginition results are compared with that of the greywolf algorithm (GWO-SVM) and the particle swarm optimization (PSO-SVM), it shows that the accuracy of the ISMA-SVM fault diagnosis and recognition is 93.3%, which is 6.66 and 10.66 percentage points higher than that of the GWO-SVM and PSO-SVM, respectively.
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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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