Dynamic Risk Assessment of Wind Farms Under Extreme Gust Disturbances Using BiLSTM, Adaboost, and Adaptive Kernel Density Estimation for Enhanced Prediction

Under extreme gusty weather conditions, wind power output fluctuates significantly, and the operational risks of wind farms exhibit strong randomness and complexity. Addressing the issue of limited sample data under such extreme wind speed conditions, which leads to low accuracy in risk assessment, this study proposes a multi-dimensional risk assessment method for wind farms based on the BiLSTM-Adaboost-ABKDE algorithm framework and ITA-TPW (Integrated Trend Analysis—Time-Series Probability Weighting) weighted fusion. First, a wind farm operational risk assessment model based on a multi-dimensional risk indicator system is constructed to assess the impact of these extreme weather conditions on wind farm operational risks. Second, to enhance the robustness of wind power output prediction under small-sample conditions, a BiLSTM (Bidirectional Long Short-Term Memory) network is introduced to capture the bidirectional temporal dependencies of wind power output. Through the Adaboost weighted ensemble of sub-models, combined with the ABKDE (Adaptive Bandwidth Kernel Density Estimation) algorithm for probability density estimation of prediction outputs, high-precision point prediction and uncertainty quantification are unified. Furthermore, we combine trend analysis sensitivity analysis using the ITA method with time-series probability weighting using the TPW method to assess key risks such as voltage overlimit, power flow overlimit, and load imbalance in wind farms under extreme wind speeds from multiple perspectives. The dataset used in this study comes from Hebei Province, covering wind power data from 1 March 2020 to 31 May 2023, and the simulation platform used is MATLAB R2023a. Simulation analysis was conducted using the IEEE RTS-79 node system to validate the effectiveness of the proposed method. The results showed that the proposed method improved the accuracy of load shedding risk and voltage overlimit risk indicators by 6.15% and 4.79%, respectively. Additionally, the reliability of the system’s comprehensive risk indicators was significantly enhanced, validating the effectiveness of this method in improving the accuracy and reliability of operational risk assessment for wind farms under extreme weather conditions.