Cleaning of Abnormal Wind Speed Power Data Based on Quartile RANSAC Regression

Fengjuan Zhang, Xiaohui Zhang (), Zhilei Xu, Keliang Dong, Zhiwei Li and Yubo Liu
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Fengjuan Zhang: College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
Xiaohui Zhang: College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
Zhilei Xu: College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
Keliang Dong: College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
Zhiwei Li: College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
Yubo Liu: College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China

Energies, 2024, vol. 17, issue 22, 1-14

Abstract: The combined complexity of wind turbine systems and harsh operating conditions pose significant challenges to the accuracy of operational data in Supervisory Control and Data Acquisition (SCADA) systems. Improving the precision of data cleaning for high proportions of stacked abnormalities remains an urgent problem. This paper deeply analyzes the distribution characteristics of abnormal data and proposes a novel method for abnormal data cleaning based on a classification processing framework. Firstly, the first type of abnormal data is cleaned based on operational criteria; secondly, the quartile method is used to eliminate sparse abnormal data to obtain a clearer boundary line; on this basis, the Random Sample Consensus (RANSAC) algorithm is employed to eliminate stacked abnormal data; finally, the effectiveness of the proposed algorithm in cleaning abnormal data with a high proportion of stacked abnormalities is verified through case studies, and evaluation indicators are introduced through comparative experiments to quantitatively assess the cleaning effect. The research results indicate that the algorithm excels in cleaning effectiveness, efficiency, accuracy, and rationality of data deletion. The cleaning accuracy improvement is particularly significant when dealing with a high proportion of stacked anomaly data, thereby bringing significant value to wind power applications such as wind power prediction, condition assessment, and fault detection.

Keywords: data cleaning; quartile; RANSAC; wind power curve; wind turbine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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