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Aiming at the problem that the transformer life prediction model has low fit and ignores the influence of the operating state, a residual life prediction model (POA-RF model) based on pelican algorithm (POA) optimized random forest (RF) model was proposed. Taking preventive tests, online monitoring, defect reports, and account data as research objects, a transformer health index system consisting of transformer body, oil quality, and dissolved gas in oil health indices was constructed. Considering the corrective effect of operating status on residual life, with relevant parameters of the health index system as inputs, the POA was used to optimize the RF model to solve its overfitting problem and improve prediction accuracy. Experiments are conducted with the collected transformer instance data to verify the accuracy of the model. The results show that for the 8 groups of scrapped samples in the test samples, the average absolute error of the residual life predicted by the POA-RF model is 1.187 0 years, which is 20.52% and 49.02% lower than the predicted value of the RF model and the calculated value of the health index theory, respectively. For all the test samples, compared with other commonly used machine learning algorithms, the relevant performance indicators of the POA-RF model are all the best, and the optimization effect is obvious, which can better predict the residual life of transformer.
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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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