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Due to the wide variety of wind farm equipment and complex operating environment, it is usually unattended and difficult to find faults in time. The traditional inspection method takes a long time and has low identification accuracy. As a result, the fault is not handled in time, which affects the stable operation and power generation efficiency of wind farms. Therefore, a robot centralized inspection scheme based on improved pattern recognition is proposed for unattended wind farm groups. For transformer faults, equipment temperature anomalies and gearbox sound anomalies in wind farms, BP neural network algorithm, fuzzy pattern recognition algorithm and empirical mode decomposition algorithm are used to carry out inspection, and the proposed method is tested experimentally in a large wind power station. The results show that the proposed method can realize the inspection of various faults in wind farms. The first time to obtain the fault signal, to avoid the occurrence of security accidents; The recognition accuracy rate remains above 92.3%, and the recall rate and F1 score are also better than the comparison method, indicating that the proposed method is more comprehensive in identifying fault samples and can detect faults more effectively.
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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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