Open AccessReal time outlier monitoring for power transformer fault diagnosis based on isolated forest
Author(s) -
Li Shen,
Hongjun Du,
Shuji Liu,
Shuo Chen,
Lin Qiao,
Sai Liu,
Jiahua Liu,
Kexin Li,
Jing Li
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/715/1/012033
Subject(s) - outlier , transformer , anomaly detection , computer science , data mining , real time computing , artificial intelligence , reliability engineering , engineering , electrical engineering , voltage
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.