Real time outlier monitoring for power transformer fault diagnosis based on isolated forest
Year: 2020
Type: conference-paper
Abstract: In order to improve the accuracy and efficiency of transformer fault detection, this paper uses the isolated forest algorithm combined with the historical transformer characteristic gas data to establish the characteristic gas outlier recognition model, and then uses the uncoded ratio method to establish the abnormal event strategy and abnormal event library for the transformer historical fault information. Finally, the state of the transformer is diagnosed based on the established outlier detection model and the exception event library. The experimental results show that the proposed method has a great improvement in the detection efficiency and stability of outliers, and is more accurate in transformer fault diagnosis combine with abnormal event database.
Authors: Li Shen, Hongjun Du, Shuji Liu, Shuo Chen, Lin Qiao, Sai Liu, Jiahua Liu, Kexin Li, Jing Li
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2996776532