基于MADM-QM的风电机组风功率异常数据处理方法  

Wind speed-power abnormal data processing method of wind turbine based on MADM-QM

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作  者:莫丰源 王卫华 郭前 Mo Fengyuan;Wang Weihua;Guo Qian(School of Mechanical Engineering,Hunan University of Science and Technology,Xiangtan 411201,China;Laboratory Development and Management Centre,Suzhou City University,Suzhou 215104,China;School of Intelligent Manufacturing and Smart Transportation,Suzhou City University,Suzhou 215104,China)

机构地区:[1]湖南科技大学机电工程学院,湖南湘潭411201 [2]苏州城市学院实验室建设与管理中心,江苏苏州215104 [3]苏州城市学院智能制造与智慧交通学院,江苏苏州215104

出  处:《可再生能源》2025年第3期339-345,共7页Renewable Energy Resources

基  金:江苏省高等学校基础科学(自然科学)研究面上项目(23KJB510026)。

摘  要:针对风电机组非正常运行时导致远程中央监控与数据采集(SCADA)系统所采集的风速-功率数据中存在大量的横向、纵向分布的异常值问题,文章提出了一种基于中值绝对偏差法(MADM)和四分位法(QM)的异常数据清洗方法,即MADM-QM算法。首先,基于风速-桨距角关系模型,通过对风速区间的风速-桨距角数据集中绝对中位差(MAD)的求解,清洗掉±4.5MAD外的风速-桨距角数据;然后,基于风速-功率关系模型,先对功率区间的风速-功率数据集中异常值进行剔除,再对风速区间的风速-功率数据集中异常值进行剔除,完成异常数据的清洗;最后,以某风电场复杂工况下风电机组的实际运行数据为算例进行验证,并与MADM,QM和基于密度的空间聚类(DBSCAN)法进行对比分析。结果表明,MADM-QM算法不仅能够有效识别异常数据,而且能够高效完成异常数据清洗,相比其他3种方法,MADM-QM算法处理异常数据效率良好且清洗质量最优。Aiming at the problem that there are a large number of horizontal or vertical distribution outliers in the wind speed-power data collected by SCADA system when wind turbine is in abnormal operation, an abnormal data processing method based on median absolute deviation method(MADM) and quartile method(QM) is proposed to solve it, namely MADM-QM algorithm. Firstly, based on the relationship model of wind speed-pitch angle, the wind speedpitch angle data outside of ±4.5 MAD are discarded by solving the median absolute deviation(MAD) in the wind speed-pitch angle data set of the wind speed interval. Secondly, based on the wind speed-power relationship model, the abnormal values in the wind speed-power data set of the power interval are eliminated, and then the abnormal values in the wind speed-power data set of the wind speed interval are eliminated to complete the abnormal data processing. Finally, the actual operation data of wind turbine under complex working conditions of a wind farm are taken as examples for verification, and comparison with MADM, QM and density-based spatial clustering(DBSCAN) method. The results indicate that the proposed method can not only effectively identify abnormal data but also efficiently and stably clean them. Compared with the other three methods, to a certain extent, it proves that MADM-QM can achieve good efficiency of abnormal data processing and optimal cleaning quality on the abnormal data.

关 键 词:风电机组 风功率 数据清洗 MADM-QM SCADA数据 

分 类 号:TK81[动力工程及工程热物理—流体机械及工程]

 

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