低温天气下考虑风机运行状态聚类的短期风电功率预测方法  

Short-Term Wind Power Prediction Method Considering Wind Turbine Operation Status Clustering Under Low-Temperature Conditions

作　　者：张扬帆[1] 李奕霖 叶林[2] 付雪姣 王正宇[1] 王耀函 ZHANG Yangfan;LI Yilin;YE Lin;FU Xuejiao;WANG Zhengyu;WANG Yaohan(North China Electric Power Research Institution(State Grid Jibei Electric Power Research Institute),Xicheng District,Beijing 100018,China;College of Information and Electrical Engineering,China Agricultural University,Haidian District,Beijing 100083,China)

机构地区：[1]华北电力科学研究院有限责任公司(国网冀北电力科学研究院),北京市西城区100018 [2]中国农业大学信息与电气工程学院,北京市海淀区100083

出　　处：《发电技术》2025年第2期326-335,共10页Power Generation Technology

基　　金：华北电力科学研究院有限责任公司科技项目(KJZ2022060)。

摘　　要：【目的】低温天气给包含高比例风电等新能源的电力系统运行带来了挑战,提升低温天气下的短期风电功率预测精度,将为电力系统的调度运行提供有效的决策信息。为此,提出一种低温天气下考虑机组运行状态聚类的风电功率预测方法。【方法】利用机组运行状态与保护控制信息,采用模糊C均值(fuzzy C-means,FCM)聚类算法对风电机组进行聚类;提出一种基于支持向量机的风机运行状态分组预测方法,预测风机是否处于正常运行状态;采用集成学习中的LightGBM算法预测风机正常运行时的功率值;基于运行状态和功率值的预测结果,给出风电场总体输出功率。最后,以冀北某风电场为例进行分析,验证所提方法的有效性。【结果】所提方法充分利用风机低温保护控制行为特征,准确预测了风电机组的关键切机时间,并给出停机容量,有效地拟合了风电功率曲线变化规律,将风电功率预测精度提升至90%以上。【结论】所提方法可为电力调度控制提供有效预测信息,也为大风等其他极端天气下的短期风电功率预测提供了参考。[Objectives]Low-temperature weather poses challenges to the operation of power systems with a high proportion of new energy,such as wind power.Improving the accuracy of short-term wind power prediction under lowtemperature conditions will provide effective decision-making information for power system scheduling and operation.To address this,a wind power prediction method considering the clustering of unit operation status under low-temperature conditions is proposed.[Methods]The fuzzy C-means(FCM)clustering algorithm is used to cluster wind turbines based on their operation status and protection control information.Then,a prediction method based on support vector machine is proposed to predict whether the wind turbines are in normal operation status.The LightGBM algorithm in ensemble learning is employed to predict the power output of wind turbines under normal operation.Based on the prediction results of both operation status and power values,the overall wind power output of the wind farm is determined.Finally,a case study of a wind farm in northern Hebei is conducted to validate the effectiveness of the proposed method.[Results]By fully utilizing the characteristics of wind turbine protection control behaviors under low temperatures,the proposed method accurately predicts the critical shutdown time of wind turbines and provides the shutdown capacity.It effectively fits the variation patterns of wind power curves,which improves the prediction accuracy of the wind power to more than 90%.[Conclusion]The proposed method can provide reliable prediction information for power scheduling and control.Additionally,it can provide a reference for shortterm wind power prediction under other extreme weather conditions,such as strong winds.

关 键 词：新能源 电力系统 风电 功率预测 机组运行 模糊C均值(FCM)聚类 支持向量机 电力调度控制 

分 类 号：TK81[动力工程及工程热物理—流体机械及工程] TM614[电气工程—电力系统及自动化]