Overload Analysis of Distribution Transformers Based on Data Mining

2018 International Conference on Advanced Electronic Materials, Computers and Materials Engineering

材料科学;无线电电子学;计算机科学

Chen, Nan^1 ; Dai, Taotao^2 ; Wang, Liancheng^1 ; Zhao, Wei^1 ; Lu, Ke^1

School of Electrical Engineering, Shandong University, Jing Shi Road No. 17923, Jinan, Shandong, China^1

Shandong Hising Electric Power Tech Co., Ltd., Orson Mansion, Jinan, Shandong, China^2

关键词: Apriori algorithms;    Corresponding measures;    Data mining models;    Data preprocessing;    Distribution transformer;    Overload analysis;    Power supply reliability;    Regional distribution;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/439/3/032112/pdf
DOI  :  10.1088/1757-899X/439/3/032112

来源: IOP

【 摘 要 】

Overload problems of distribution transformers frequently occur in distribution networks. To avoid the in-advertent effect on the networks and take corresponding measures, the association rules are used to analyze the heavy overload phenomenon of distribution transformers. For the operation of the distribution network, it is very important to study the strong association rules between the heavy overload phenomenon of the transformer in different areas and the seasons, weather and holidays. In this paper, the data preprocessing of heavy overload data and other data of transformer network is first processed, and then a data mining model is established. Finally, the strong association rules of heavy overload are found by using Apriori algorithm. The strong association rules can be used to guide the operation of regional distribution network and avoid the influence of heavy load overload on power supply reliability.