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The change in wind direction poses a challenge to the yaw control of wind turbines. Due to frequent changes in wind direction, the response speed of wind turbines is too slow, making it difficult to adjust the yaw angle in a timely manner, thereby affecting power generation efficiency. In order to accurately control the problem of wind turbine wheel yaw, a collaborative intelligent control method for wind turbine wheel yaw considering the randomness of wind speed is proposed. Considering the randomness of wind speed, analyze the wind direction signal and obtain wind direction sample data. Introduce a Bayesian classifier and combine it with a wind direction normal analysis model to calculate the posterior probability of wind direction samples that follow the distribution of the previous batch of samples. Use it as the benchmark for adjusting the warning value, establish a network warning value adjustment mechanism based on Bayesian inference, and adjust the warning value through a mountain climbing algorithm to achieve collaborative intelligent control of wind turbine rotor yaw. The experimental results show that the proposed method achieves collaborative intelligent control of wind turbine rotor yaw, with zero occurrence of yaw in the cabin position and a short yaw control time. This indicates that the method can achieve collaborative intelligent control of wind turbine rotor yaw.
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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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