Open AccessOutlier detection for on-line monitoring data of transformer based on wavelet transform and weighted LOF
Author(s) -
Jiajun Qin,
Yi Yang,
Du Hong,
Zong-Wei Hong
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/354/1/012108
Subject(s) - outlier , transformer , wavelet , computer science , wavelet transform , anomaly detection , transformer oil , local outlier factor , data mining , artificial intelligence , engineering , voltage , electrical engineering
Power transformers are one of the core equipment in the power grid, so it is of great significance to guarantee transformers’ normal operation. By analyzing the dissolved gas content in transformer oil, we can monitor the operation of the power transformer. However, usually there are outliers in the data generated using the on-line monitoring system. In this paper, we propose a new outlier detection method based on wavelet transform and local outlier factor (LOF) algorithm. Using wavelet transform, we get the high dimensional representation of the original data in frequency domain, and by adding the weighted LOF (WLOF), we can identify outliers in high dimensional data set. Furthermore, we use the sliding window method to improve the efficiency of the algorithm, and achieve transformer oil on-line outlier detection efficiently. The experimental results on transformer data from several power transformers indicate that this algorithm can identify the outliers that exceed the threshold value, as well as the oscillations due to fluctuations in gas content. This can help achieve initial diagnosis of transformer oil on-line monitoring system rapidly.