DGA and Weibull Distribution Model-based Transformer Fault Early Warning

2019 2nd International Conference on Advanced Materials, Intelligent Manufacturing and Automation

Yang, Pei^1^2 ; Pan, Sen^1^2 ; Jiang, Jing^1^2 ; Rao, Wei^1^2 ; Qiao, Junfeng^1^2

Global Energy Interconnection Research Institute Co. Ltd, Beijing

102209, China^1

Artificial Intelligence on Electric Power System State Grid Corporation Joint Laboratory, Beijing

102209, China^2

关键词: Calculation formula;    Chromatographic data;    Distribution models;    Fault early warnings;    Fault rates;    Stable operation;    Transformer faults;    Volume distributions;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/569/3/032072/pdf
DOI  :  10.1088/1757-899X/569/3/032072

来源: IOP

【 摘 要 】

As an important power transmission and transformation equipment, transformer fault has a great impact on the safe and stable operation of smart grid and probably incurs serious consequences. Therefore, how to detect and warn the fault of transformers as early as possible becomes particularly critical. In this paper, a fault early warning method based on DGA and Weibull distribution model is proposed for a large number of transformers in smart grid and the calculation formulas of the attention value and warning value of transformer fault are given. First, the defect rate and fault rate of transformers can be obtained by analysing the transformer maintenance data. Then, the attention value and warning value of transformer fault are calculated with the Weibull distribution model according to the gases volume distribution of oil chromatographic data, which provides an effective method for the transformer fault early warning. The actual case study shows that the proposed method can effectively achieve the transformer fault early warning.