Short-Term Power Forecasting for Wind Power Generation under Extreme Weather Conditions

Extreme weather poses a great challenge to the safe operation of wind power and new power systems dominated by wind power. Providing accurate wind power prediction will be an effective response. For this reason, this paper proposed a short-term power forecasting for wind power generation under extreme weather conditions. Firstly, the relationship between wind speed and wind turbine output power is analyzed and a wind power generation model is established. Then, the Long Short-Term Memory Neural Network (LSTM) is applied to construct a short-term power prediction model for wind turbines, and establishes three models for wind turbine decommissioning under extreme weather. Moreover, the climbing control strategy of wind turbine is also investigated to guarantee the system safety, stability and operation economy. Finally, the numerical analysis is carried out on the wind farm consists of 28 wind turbines, the results verify the effectiveness and superiority of the proposed method.