A Fast Abnormal Data Cleaning Algorithm for Performance Evaluation of Wind Turbine

TLDR

The paper proposes a fast image‑processing algorithm to clean abnormal wind turbine data for performance evaluation. The method comprises two stages—data cleaning, where normal data pixels are extracted from wind power curve images, and data classification, where points are labeled based on pixel presence; the cleaning is accelerated by GPU parallelization using CUDA. Computational results demonstrate superior abnormal data cleaning performance with dramatically reduced execution time, making the approach practical for real‑world wind turbine performance monitoring.

Abstract

A fast wind turbine abnormal data cleaning algorithm via image processing for wind turbine power generation performance measurement and evaluation is proposed in this paper. The proposed method includes two stages, data cleaning and data classification. At the data cleaning stage, pixels of normal data are extracted via image processing based on pixel spatial distribution characteristics of abnormal and normal data in wind power curve (WPC) images. At the data classification stage, wind power data points are classified as normal and abnormal based on the existence of corresponding pixels in the processed WPC image. To accelerate the proposed method, the cleaning operation is executed parallelly using graphics processing units (GPUs) via compute unified device architecture (CUDA). The effectiveness of the proposed method is validated based on real data sets collected from 37 wind turbines of two commercial farms and three types of GPUs are employed to implement the proposed algorithm. The computational results prove the proposed approach has achieved better performance in cleaning abnormal wind power data while the execution time is tremendously reduced. Therefore, the proposed method is available and practical for real wind turbine power generation performance evaluation and monitoring tasks.