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With the continuous development of renewable energy sources and the increasing share of renewable energy in the grid，optimal coordination of maintenance work becomes increasingly important in order to ensure the safety of power supply in power systems considering renewable energy access. Current tools for maintenance planning are constrained by operational safety standards and the complexity of the grid，and have problems such as low operability and high computational effort to simulate accidents. To reduce the burden of manual computation，the use of machine learning models was proposed to predict the outcome of emergency situations in a fast and reliable manner. The method was tested in a regional facility in Lanzhou，covering voltage levels of 10 kV and 220 kV. By testing and comparing a plain Bayesian classifier，a support vector machine （SVM）and a decision tree-based model，it was shown that the decision tree-based random forest algorithm is consistently better than other algorithms in identifying safe serviceable time periods with an accuracy rate higher than 90%. In addition，it was shown experimentally that the expected growth in renewable energy generation will affect the future serviceability of the power system，with a 20% increase in non-safe serviceable time periods in some areas.
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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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