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

Luo Langchuan; Li Ruhui; Zeng Dong; Zou Mingheng

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

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (4) : 445-453. DOI: 10.19912/j.0254-0096.tynxb.2023-2072

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

Luo Langchuan¹, Li Ruhui¹, Zeng Dong¹, Zou Mingheng²

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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.

Key words

offshore wind farms / data analytics / anomaly detection / RANSAC / DBSCAN

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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

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