A Data Cleaning Model for Electric Power Big Data Based on Spark Framework

Abstract

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.