基于RANSAC-DBSCAN的风速功率曲线异常数据清洗方法

罗朗川; 李汝辉; 曾东; 邹明衡

doi:10.19912/j.0254-0096.tynxb.2023-2072

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (4) : 445-453. DOI: 10.19912/j.0254-0096.tynxb.2023-2072

基于RANSAC-DBSCAN的风速功率曲线异常数据清洗方法

罗朗川¹, 李汝辉¹, 曾东¹, 邹明衡²

作者信息 +

ABNORMAL DATA CLEANING METHOD OF WIND SPEED-POWER CURVE BASED ON RANSAC-DBSCAN

Luo Langchuan¹, Li Ruhui¹, Zeng Dong¹, Zou Mingheng²

Author information +

文章历史 +

摘要

针对海上风电机组在运行中不可避免地产生大量噪声、故障、弃风限电等异常数据,导致运行数据可用性差的问题。梳理和分析风功率曲线中异常数据的分布特征,提出基于随机采样一致（RANSAC）回归与含噪声的基于密度的空间聚类（DBSCAN）融合算法的风功率曲线异常数据清洗,并从算法的清洗效果、清洗效率以及数据删除合理性进行对比验证。结果表明,所提方法能够快速、简便、合理地识别异常数据范围,具有工程应用价值。

Abstract

Addressing the challenge of offshore wind turbines inevitably producing a significant volume of abnormal data—such as noise, faults, wind abandonment, and power limitation, which compromises the availability of operational data—this paper analyzes the distribution characteristics of anomalous data within wind power curves. We propose a novel method for cleaning this data, utilizing a fusion algorithm that combines Random Sample Consensus （RANSAC） and Density-Based Spatial Clustering of Applications with Noise （DBSCAN）. This approach is rigorously evaluated for its cleaning effectiveness, efficiency, and the justification of data exclusion. Our findings reveal that the proposed methodology can swiftly, effortlessly, and logically delineate the boundaries of anomalous data, demonstrating its significant potential for engineering applications.

导出引用

罗朗川, 李汝辉, 曾东, 邹明衡. 基于RANSAC-DBSCAN的风速功率曲线异常数据清洗方法[J]. 太阳能学报. 2025, 46(4): 445-453 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2072

Luo Langchuan, Li Ruhui, Zeng Dong, Zou Mingheng. ABNORMAL DATA CLEANING METHOD OF WIND SPEED-POWER CURVE BASED ON RANSAC-DBSCAN[J]. Acta Energiae Solaris Sinica. 2025, 46(4): 445-453 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2072

中图分类号： TK89

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