电气传动2025，Vol.55Issue(6)：19-24,6.DOI:10.19457/j.1001-2095.dqcd25930
基于随机森林和改进高斯过程的风电机组功率曲线模型
Wind Turbine Power Curve Model Based on Random Forest and Improved Gaussian Process
许灿 1缪书唯1
作者信息
- 1. 三峡大学电气与新能源学院,湖北 宜昌 443002
- 折叠
摘要
Abstract
Wind turbine condition monitoring and wind power prediction both rely heavily on power curves.Firstly,to increase the modeling accuracy of wind turbine power curves,the random forest technique was used to screen the important variables that influence wind energy capture ability.Then,the screened variables were fed into the improved Gaussian process(GP)model,which improved computational efficiency.Finally,four separate metrics were used to evaluate the model's correctness,and the entropy weight approach was used to resolve any potential conflicts between the metrics,resulting in a comprehensive assessment metric that measured the quality of the power curve model.The suggested approach's effectiveness was validated using supervisory control and data acquisition(SCADA)data from a wind farm in the United Kingdom,and the findings reveal that the proposed method improves model accuracy when compared to the current six types of conventional methods.关键词
风电机组/功率曲线/随机森林/改进高斯过程/熵权法Key words
wind turbine/power curve/random forest/improved Gaussian process/entropy weight method分类
能源与动力引用本文复制引用
许灿,缪书唯..基于随机森林和改进高斯过程的风电机组功率曲线模型[J].电气传动,2025,55(6):19-24,6.
