Earticle

초록

영어

The data cleaning of electrical power big data can improve the correctness, the completeness, the consistency and the reliability of the data. Aiming at the difficulties of the extracting of the unified anomaly detection pattern and the low accuracy and continuity of the anomaly data correction in the process of the electrical power big data cleaning, the data cleaning model of the electrical power big data based on Spark is proposed. Firstly, the normal clusters and the corresponding boundary samples are obtained by the improved CURE clustering algorithm. Then, the anomaly data identification algorithm based on boundary samples is designed. Finally, the anomaly data modification is realized by using exponential weighting moving mean value. The high efficiency and accuracy is proved by the experiment of the data cleaning of the wind power generation monitoring data from the wind power station.

목차

Abstract
 1. Introduction
 2. Data Cleaning Model for Electric Power Big Data Based on Spark Framework
 3. Normal Cluster Sample Acquisition Algorithm
 4. Algorithm for Anomaly Data Identification Based on Boundary Samples
 5. Anomaly Data Modification Based on Time Series Analysis
 6. Experiment and Result Analysis
 7. Conclusion
 References

키워드

저자

• Zhao-Yang Qu [ School of Information Engineering of Northeast Dianli University, Jilin 132012, China ]
• Yong-Wen Wang [ School of Information Engineering of Northeast Dianli University, Jilin 132012, China ]
• Chong Wang [ Information &Telecommunication Branch Company, State Grid East Inner Mongolia Electric Power CO.LTD, 010020 Hohhot, China ]
• Nan Qu [ Repair Branch Company, State Grid Jiangsu Electric Power Company, 210000 Nanjing, China ]
• Jia Yan [ State Grid Jilin Electric power Supply Company, 130000 Changchun, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605