A Method of Prediction for Transformer Malfunction Based on Oil Chromatography

Wu, Hao; Zhou, Yang; Yang, Chuanqi; Zhu, Hongmei; Hao, Dongxin; Ren, Shuangzan

doi:10.1109/cacre50138.2020.9230296

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The methods can be divided into two categories. One method still uses the gas concentration or gas concentration ratio at a single moment as the feature, and a time-series model such as LSTM is used to introduce the information of the past moment into the current diagnosis time point so as to realise the comprehensive utilisation of the time-series data information and improve the diagnostic performance [24]. Another method is to explore new features from the entire time-series scale and then use the features to diagnose transformer faults.…”

Section: Introductionmentioning

confidence: 99%

New correlation features for dissolved gas analysis based transformer fault diagnosis based on the maximal information coefficient

Liang

Zhang

et al. 2021

High Voltage

View full text Add to dashboard Cite

Online monitoring of gases dissolved in transformer oil is widely applied. Improving the performance of dissolved gas analysis (DGA)-based fault diagnosis methods by exploring new features of time-series data has become an appealing topic. In this study, a new type of correlation features between characteristic gases was extracted from timeseries data based on the maximal information coefficient (MIC), and a fuzzy inference system was established. After the introduction of the principle of the MIC and a method for calculating the MIC-based correlation features, the dominant symptom features that can be used to classify fault types were extracted through the receiver operating characteristic curve. Then, fuzzy rules were learnt, and a fuzzy inference system was designed. In addition, to improve the feasibility of the method, the Newton interpolation method was used for adaptation to the existing sampling cycle. The diagnostic results of the test data show that the proposed method has excellent performance and outperforms some prevailing traditional rule-based methods as well as some artificial intelligent methods. The results also show that by exploring new correlation features from time-series data based on the MIC, the performance of DGAbased methods can be improved.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

show abstract

Section: Introductionmentioning

confidence: 99%