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Generator is an important core component in wind power system, in order to improve the stable and efficient operation of wind turbine, the fault prediction of wind turbine generator is necessary. Focusing on the problem of generator machineside bearing temperature overrun fault prediction in wind power system, this paper takes into account that the collected fault characteristic signal is characterized by large noise, introduces CEEMDAN joint adaptive wavelet threshold denoising method to realize effective denoising of the signal, and at the same time establishes a fault prediction model by combining GABP neural network. By comparing the prediction indexes, error indexes and prediction effect graphs with BP neural network and GABP neural network, it is verified that the proposed algorithm can obtain better prediction effect. The error index and prediction effect are improved, and the accuracy of the prediction of generator failure of wind power system 15 days in advance reaches 92.98%.
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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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