摘要
针对数据驱动的风电功率预测模型中,高维异质大数据特征信息挖掘问题,提出考虑机组动态特性的轻量梯度上升学习机(LGBM)预测模型和区间估计的不确定性量化方法。首先,设置发电机转速、叶片角度为机组动态特性指标,构建LGBM超短期风电功率预测模型。其次,采用模糊C均值聚类对历史预测出力和预测误差样本进行区间划分;考虑预测出力和预测误差条件相依性,采用非参数估计拟合误差概率分布,并以置信区间对风电功率预测区间进行了离散化表征。最后,选取实际风电场数据进行验证。结果表明:考虑机组动态特性的LGBM预测模型的精度和计算效率显著提升;基于区间估计的不确定性量化方法解耦拟合过程与预测方法,可靠性高,灵活性强。
To deeply mine the information of features in a data-driven model with high-dimensional heterogeneous data inputs,a Light Gradient Boosting Machine(LGBM)prediction model is proposed which considers the dynamic characteristics of the unit and the uncertainty quantitatively based on interval estimation.First,taking the generator rotor speed and pitch angle as the indicators of the dynamic status of the wind turbine generator,the LGBM algorithm is explored to build the prediction model.Secondly,the historical predicted power and error are divided into different groups using fuzzy C-means clustering.Then considering the conditional dependence of prediction output and error,the error probability distribution is established by nonparametric estimation,and the confidence interval is used to discretize the prediction interval of wind power.Last,a case study from an actual wind farm is conducted to test the proposed study framework.The results show that the prediction accuracy and calculation efficiency of the LGBM model considering the dynamic characteristics of unit are significantly improved,and the uncertainty quantification method based on interval estimation decouples the fitting process and prediction method.This has high reliability and flexibility.
作者
黄慧
贾嵘
师小雨
王颂凯
HUANG Hui;JIA Rong;SHI Xiaoyu;WANG Songkai(College of Electrical Engineering,Xi'an University of Technology,Xi'an 710048,China;College of Electric Power,North China University of Water Resources and Electric Power,Zhengzhou 450011,China)
出处
《电力系统保护与控制》
CSCD
北大核心
2021年第8期109-117,共9页
Power System Protection and Control
基金
国家自然科学基金项目资助(51779206)
河南省科技攻关项目资助(162102210236)。
关键词
特征选择
轻量梯度上升学习机
风电功率预测
区间估计
feature selection
light gradient boosting machine
wind power forecasting
interval estimation
