A BPNN Model-Based AdaBoost Algorithm for Estimating Inside Moisture of Oil-Paper Insulation of Power Transformer

The traditional method for transformer moisture diagnosis is to establish empirical equations between feature parameters extracted from frequency domain spectroscopy (FDS) and the transformer's moisture content. However, the established empirical equation may not be applicable to a novel testin...

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Vydané v:IEEE transactions on dielectrics and electrical insulation Ročník 29; číslo 2; s. 614 - 622
Hlavní autori: Liu, Jiefeng, Ding, Zheshi, Fan, Xianhao, Geng, Chuhan, Song, Boshu, Wang, Qingyin, Zhang, Yiyi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Aging
Algorithms
Artificial neural networks
Back propagation networks
Classification algorithms
Empirical equations
Estimation
Feature extraction
Frequency domain spectroscopy (FDS)
Insulation
Machine learning
Mathematical models
Moisture
Moisture content
Moisture effects
moisture estimation
neural network
Neural networks
oil–paper insulation
Parameters
power transformer
Power transformer insulation
Prediction algorithms
Training
Transformers
ISSN:1070-9878, 1558-4135
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Shrnutí:The traditional method for transformer moisture diagnosis is to establish empirical equations between feature parameters extracted from frequency domain spectroscopy (FDS) and the transformer's moisture content. However, the established empirical equation may not be applicable to a novel testing environment, resulting in an unreliable evaluation result. In this regard, it is acknowledged that FDS combined with machine learning is more suitable for estimating moisture content in a variety of test environments. Nonetheless, the accuracy of the estimation results obtained using the existing method is limited by the algorithm's inability to generalize. To address this issue, we propose an AdaBoost algorithm-enhanced back-propagation neural network (BP_AdaBoost). This study creates a database by extracting feature parameters from the FDS that characterize the insulation states of the prepared samples. Then, using the BP_AdaBoost algorithm and the newly constructed database, the moisture estimation models are trained. Finally, the results of the estimation are discussed in terms of laboratory and field transformers. By comparing the proposed BP_AdaBoost algorithm to other intelligence algorithms, it is demonstrated that it not only performs better in generalization, but also maintains a high level of accuracy.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1070-9878
1558-4135
DOI:10.1109/TDEI.2022.3157909