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A method of identifying data by considering the excitation characteristics is proposed to solve the problem that it is difficult to select suitable samples from the historical operation data to identify the turbine work model. Firstly, Fisher’s information matrix condition number is applied to extract the excitation characteristics of the historical operating data, which together with the trend characteristics and the correlation between parameters constitute the set of feature variables. Secondly, by using the feature variables as inputs and the identification results generated based on the standard turbine work model as outputs, the Random Forest classification algorithm is used to generate a classification rule model for the identification data to realize the online selection of identification data. Finally, the accuracy of the model classification results and the identification effect of the selected data are verified. The result proves that the accuracy of the classification rule model is 97.561%, which can accurately select the sample segments containing sufficient incentives in the historical operation data, and the identification results of the turbine work model are in high consistency with that of the the standard model.
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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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