With the gradual expansion of scale of wind turbines and the increasing improvement in safety protection of the wind turbines, it is a problem in wind turbine technology that how to improve power generation performance, such as power generation amount and availability, of the wind turbines to gain maximum energy benefit and the economic benefit.
During control of a wind turbine, wind speed determination is one of factors that determine control effect of the wind turbine. Accurate prediction of wind speed can facilitate improving a control strategy for the wind turbine to achieve a better control effect.
At present, a main control system of a wind turbine mostly performs control strategy adjustment and yawing passively based on its detected wind speed variation or wind direction variation. For example, if a main control system begins to control a pitch control system to perform a pitch variation operation after a wind speed variation is detected by a wind turbine, then a pitch variation of a blade will lag behind the wind speed variation, resulting in an unstable rotational speed of an impeller and loss of power generation amount. For another example, if a main control system begins to control a yawing system to perform yawing after a wind direction variation is detected by a wind turbine, then a yawing action will lag behind the wind direction variation, resulting in a significant decrease in a rotational speed of an impeller after the wind direction variation and resulting in loss of power generation amount.
In practice, with the increase of capability of a wind turbine, loss of power generation amount caused during long term operation is immeasurable. Therefore, a wind speed value and a wind direction value need to be predicted in a control strategy of a wind turbine, to realize active pitch variation and active yawing of the wind turbine.
Currently, there are three ways to predict a wind speed and a wind direction of a wind turbine.
The first way is using wind speed values and wind direction values measured by anemometer towers. Due to long distances between the anemometer towers and the wind turbine and the limited number of the anemometer towers, the measured wind speed values and the measured wind direction values are only considered as a reference instead of data required by controlling the wind turbine. Additionally, a distance between each anemometer tower and each wind turbine is unknown, hence it is difficult to seize the accurate moment to control or yawing in advance. What's more, due to the limited number of the anemometer towers, the anemometer towers is likely to coincide with multiple wind turbines in direction, and wind turbulence will cause a great influence and deviation on the wind speed value and the wind direction value. Besides, if a wind farm is in a mountainous region, wind speed values and wind direction values measured by the anemometer towers can not be considered as a control basis for all wind turbines as heights of the wind turbines are different.
The second way is using a weather forecast to predict the wind speed value. This prediction method is aimless, and the predicted wind speed value is quite inaccurate.
The third way is predicting the wind speed value and the wind direction value based on big data. This prediction method requires historical data of a long term operation, and has a requirement on both data quantity and quality of the historical data. In addition, the prediction method can only make a prediction with a certain probability, and can not reflect a true wind speed value and a true wind direction value. Meanwhile, as the big data includes too much historical data, prediction of the wind speed value and the wind direction value based on the big data has a certain extent of lag for controlling the wind turbine.