Abstract
Data accuracy and completeness of the wind farm has great significance in wind power research. Because of the wind farm in the process of gathering data and transmission appears distorted and missing, and that leads the accuracy and integrity of data is greatly reduced, so the need for a wind farm data, outlier detection and missing data imputation. This paper outlier detection by statistical method based on 3σ criterion under the normal distribution, and use of the effectiveness of the recently distance interpolation and regression interpolation for missing data, outliers and replacement and interpolation, filled after data and accuracy are improved.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
