基于滑动窗双边 CUSUM 算法的 风电爬坡事件检测方法.

With the advancement of new energy grid integration, the scale of wind power installations has been expanding annually. Affected by regional weather changes, the intermittency and fluctuation characteristics of wind turbine output pose an increasingly significant threat to the power grid. Extreme weather-induced wind power output anomalies, known as ramp-up events, can lead to power grid imbalances and place tremendous pressure on power system unit dispatch and load balancing. Reasonable detection of wind power ramp-up events and accurate wind power forecasting can provide prior guidance for wind farm operation and maintenance and power system dispatch, effectively alleviating the harm caused by wind power uncertainty. Firstly, the blind spots in the current mainstream definitions of wind power ramp-up events are discussed. Following this, the power change characteristics of three wind power ramp-up scenarios were classified and analyzed. Then, a method for detecting wind power ramp-up events based on sliding window bilateral Cumulative Sum(CUSUM) was proposed. This method extracts time series coupling information, captures abnormal fluctuations in wind power data within a short time window, and improves the accuracy of wind power ramp-up event detection. Furthermore, a long short-term memory (LSTM) neural network optimized by Bayesian optimization was employed to optimize model hyperparameters and improve the model􀆳s predictive performance for wind turbine output during ramp-up events. The proposed wind power ramp-up event detection method was further applied to detect wind power ramp-up events within the model prediction interval, verifying the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]