State Evaluation Method of Distribution Equipment Based on Health Index in Big Data Environment

Abstract

Aiming at the problems of time-consuming and low accuracy in the existing state evaluation methods of distribution equipment, a state evaluation method of distribution equipment based on health index in big data environment is proposed. Firstly, in order to optimize the time-consuming of big data analysis on large-scale and distributed clusters, a distribution equipment condition monitoring data platform in big data environment is designed, and a hive based relational online analysis method (ROLAP) is proposed. Secondly, the health index (HI) is introduced as the evaluation index to evaluate the health status of distribution equipment. According to the different influence degree of different fault factors on the equipment status, a comprehensive multifactor fault rate correction model is obtained, and the method based on success flow is used to solve the model to improve the accuracy of state evaluation. Finally, experiments show that when the data volume of distribution equipment is 60 GB, the time of the proposed method is only 30.0 s, which is far lower than 73.6 s and 82.5 s of the comparison method. The evaluation accuracy of the proposed method is 95.1%, while the evaluation accuracy of the comparison method is only 82.4% and 73.1%, respectively. Therefore, the proposed method can effectively improve the efficiency of distribution equipment condition evaluation.