Big Data Preprocessing Approach for Smart Grid Based on Partial Order Reduction

Abstract

Abstract In the age of big data, power grid data is characterized by large amount, fast speed and variable types. Traditional attribute reduction methods can no longer meet the needs of big data preprocessing. Therefore, this paper proposes a partial order attribute reduction method for power big data based on rough set. This method deeply analyzes the characteristics of the decision table, uses the parallelization algorithm to improve the attribute reduction algorithm, and effectively solves the efficiency problem in the computing process in the big data environment. Then it generates a series of MapReduce tasks corresponding to the generated Hive command, and obtains the decision rules through reduction. Finally, based on Hadoop platform, the incremental attribute reduction calculation of power fault data in certain area is designed according to the actual demand. The results show that the method has good performance in processing big data, and it can effectively process massive continuous power grid equipment monitoring data.