Comprehensive analysis and evaluation of big data for main transformer equipment based on PCA and Apriority

2017 3rd International Conference on Environmental Science and Material Application

生态环境科学;材料科学

Guo, Lijuan^1 ; Yan, Haijun^1 ; Hao, Yongqi^2 ; Chen, Yun^3

Guangxi Electric Power Research Institute, Nanning, China^1

School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China^2

School of Electrical Engineering, Tsinghua University, Bejing, China^3

关键词: Comprehensive analysis;    Comprehensive evaluation;    Evaluation algorithm;    Identification algorithms;    Potential dependence;    Power grid equipment;    Power system equipments;    Support and confidence;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/108/5/052026/pdf
DOI  :  10.1088/1755-1315/108/5/052026

来源: IOP

【 摘 要 】

With the power supply level of urban power grid toward high reliability development, it is necessary to adopt appropriate methods for comprehensive evaluation of existing equipment. Considering the wide and multi-dimensional power system data, the method of large data mining is used to explore the potential law and value of power system equipment. Based on the monitoring data of main transformer and the records of defects and faults, this paper integrates the data of power grid equipment environment. Apriori is used as an association identification algorithm to extract the frequent correlation factors of the main transformer, and the potential dependence of the big data is analyzed by the support and confidence. Then, the integrated data is analyzed by PCA, and the integrated quantitative scoring model is constructed. It is proved to be effective by using the test set to validate the evaluation algorithm and scheme. This paper provides a new idea for data fusion of smart grid, and provides a reference for further evaluation of big data of power grid equipment.