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

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.

Automated summary

This paper presents a fast wind turbine abnormal data cleaning algorithm via image processing for wind turbine power generation performance measurement and evaluation, which includes two stages: data cleaning and data classification. The effectiveness of the proposed method is validated based on real data sets and the computational results prove the proposed approach has achieved better performance in cleaning abnormal wind power data.