首页|一种结合图像处理与社区检测算法的风电异常数据清洗方法

一种结合图像处理与社区检测算法的风电异常数据清洗方法

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原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
目前用于风电异常数据清洗的方法在识别大规模数据集中异常数据的能力有限，并且难以适应风电场数据的高变异性和复杂性。因此，提出了一种结合图像处理与社区检测算法的风电异常数据清洗方法。为了准确识别和初步清洗异常数据，将风功率曲线图像转换成图结构，采用Louvain社区识别算法和图论方法进行社区检测和分割。然后，采用数学形态学运算确定初步清洗后的风功率曲线图像的主体部分，并将其映射回正常的风功率点，从而完成最终清洗。为验证 CWPAD-IPCDA 方法的可行性，对中国西北部两个风电场的25台风机数据集进行清洗，并与带噪声的基于密度的空间聚类算法、改进的孤立森林算法和基于图像的算法进行比较。实验结果表明，CWPAD-IPCDA方法的平均数据清洗率提高了约7。23％，清洗后数据集的误差平方和均值降低约6。887，优于其他三种算法。此外，综合准确率的均值(以F1分数衡量)相较于其他三种算法提高了约10。49％。证明了CWPAD-IPCDA方法有助于提高风功率曲线建模和风电场功率预测的准确性以及可靠性。
A method for cleaning wind power anomaly data by combining image processing with community detection algorithms
Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm data.Consequently,a method for cleaning wind power anomaly data by combining image processing with community detection algorithms(CWPAD-IPCDA)is proposed.To precisely identify and initially clean anomalous data,wind power curve(WPC)images are converted into graph structures,which employ the Louvain community recognition algorithm and graph-theoretic methods for community detection and segmentation.Furthermore,the mathematical morphology operation(MMO)determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final cleaning.The CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines(WTs)in two wind farms in northwest China to validate its feasibility.A comparison was conducted using density-based spatial clustering of applications with noise(DBSCAN)algorithm,an improved isolation forest algorithm,and an image-based(IB)algorithm.The experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms,achieving an approximately 7.23%higher average data cleaning rate.The mean value of the sum of the squared errors(SSE)of the dataset after cleaning is approximately 6.887 lower than that of the other algorithms.Moreover,the mean of overall accuracy,as measured by the F1-score,exceeds that of the other methods by approximately 10.49%;this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting.

Wind turbine power curveAbnormal data cleaningCommunity detectionLouvain algorithmMathematical morphology operation

杨巧玲、陈凯、满建樟、段佳恒、金作启

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College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,P.R.China

风电机组功率曲线 异常数据清洗 社区检测 Louvain算法 数学形态学运算

National Natural Science Foundation of ChinaNatural Science Foundation of Gansu Province

Project 51767018Project 23JRRA836

2024

10.1016/j.gloei.2024.06.001

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全球能源互联网(英文)

CSTPCDEI
ISSN：2096-5117
年,卷(期)：2024.7(3)