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To establish an accurate and effective dynamic model of cogeneration units, a modeling method based on digital twin technology is proposed using unit operation data. Firstly, the historical data stored in the unit data server is extracted, it is then clustered using the improved genetic simulated annealing fuzzy C-means method to establish a historical data clustering library. Then, during the operation of the unit, real-time operational data is collected and transmitted, and a multi-level similarity recognition strategy is used to retrieve the historical data closest to real-time operational data in the historical data clustering library. Then, based on the optimization, the extreme learning machine will use the searched historical data for unit modeling. Finally, a twin model of a cogeneration unit in Hangzhou is established and comparative experiments are conducted. The results show that, the built model meets the accuracy requirements and can track the real-time state response of the unit. The model accuracy can be further optimized by flexibly changing the parameter settings during the modeling process.
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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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