高压电器2025，Vol.61Issue(5)：179-188,10.DOI:10.13296/j.1001-1609.hva.2025.05.019
考虑热带气旋下爬坡致因的风电功率爬坡直接预测
Direct Predication of Wind Power Ramp Considering the Cause of Tropical Cyclone Ramping Downhill
摘要
Abstract
The strong wind weather phenomenon due to tropical cyclone leads to abnormal output power variation of offshore wind farms,forms into a wind power ramp phenomenon and seriously threatens safe operation of the main power grid on shore.Based on this,a forecasting method for offshore wind power ramp events based on key creep causes is proposed by analyzing and constructing creep causes considering typical meteorological factors of tropical cyclone.Firstly,the influence of typical meteorological factors on wind power and its ramp events under tropical cy-clone conditions is analyzed,and a wind power ramp events attribution model and key features of ramp events causes are constructed.Then,based on the key characteristics of s ramp events causes,a cost-sensitive logistic regression model,a weighted plain Bayesian model and a cost-sensitive random forest model are adopted to construct a base model for direct ramp events prediction.Finally,the integrated learning method based on bagging is used to improve the robustness and prediction accuracy of the model.The numerical results show that,compared to the feature triplet and the feature quintuples,the accuracy of the base model can be improved by 16.63％,10.94％,and 2.52％,respec-tively.Moreover,compared to the base model,the combined model can guarantee the robustness under the signal-to-noise ratio from 5 dB to 25 dB,and the accuracy of the combined model reaches up to 93％at the signal-to-noise ratio of 25 dB,which confirms the applicability of the proposed model in unbalanced data sets and noisy environments.关键词
热带气旋/风电功率爬坡/直接爬坡预测/集成学习Key words
tropical cyclones/wind power ramp/direct ramp predication/ensemble learning引用本文复制引用
严佳丽,任必兴,陈思媛,崔明建,陈春宇..考虑热带气旋下爬坡致因的风电功率爬坡直接预测[J].高压电器,2025,61(5):179-188,10.基金项目
国家自然科学基金项目(52207142,52207130) （52207142,52207130）
江苏省自然科学基金项目(BK20210512).Project Supported by National Natural Science Foundation of China(52207142,52207130),Natural Science Foundation of Jiangsu Province(BK20210512). （BK20210512）
