- Title
- Assessing transformer oil quality using deep convolutional networks
- Creator
- Alam, Mohammad; Karmakar, Gour; Islam, Syed; Kamruzzaman, Joarder; Chetty, Madhu; Lim, Suryani; Appuhamillage, Gayan; Chattopadhyay, Gopi; Wilcox, Steve; Verheyen, Vincent
- Date
- 2019
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/172886
- Identifier
- vital:14574
- Identifier
-
https://doi.org/10.1109/AUPEC48547.2019.211896
- Identifier
- ISBN:9781728150437 (ISBN)
- Abstract
- Electrical power grids comprise a significantly large number of transformers that interconnect power generation, transmission and distribution. These transformers having different MVA ratings are critical assets that require proper maintenance to provide long and uninterrupted electrical service. The mineral oil, an essential component of any transformer, not only provides cooling but also acts as an insulating medium within the transformer. The quality and the key dissolved properties of insulating mineral oil for the transformer are critical with its proper and reliable operation. However, traditional chemical diagnostic methods are expensive and time-consuming. A transformer oil image analysis approach, based on the entropy value of oil, which is inexpensive, effective and quick. However, the inability of entropy to estimate the vital transformer oil properties such as equivalent age, Neutralization Number (NN), dissipation factor (tanδ) and power factor (PF); and many intuitively derived constants usage limit its estimation accuracy. To address this issue, in this paper, we introduce an innovative transformer oil analysis using two deep convolutional learning techniques such as Convolutional Neural Network (ConvNet) and Residual Neural Network (ResNet). These two deep neural networks are chosen for this project as they have superior performance in computer vision. After estimating the equivalent aging year of transformer oil from its image by our proposed method, NN, tanδ and PF are computed using that estimated age. Our deep learning based techniques can accurately predict the transformer oil equivalent age, leading to calculate NN, tanδ and PF more accurately. The root means square error of estimated equivalent age produced by entropy, ConvNet and ResNet based methods are 0.718, 0.122 and 0.065, respectively. ConvNet and ResNet based methods have reduced the error of the oil age estimation by 83% and 91%, respectively compared to that of the entropy method. Our proposed oil image analysis can calculate the equivalent age that is very close to the actual age for all images used in the experiment. © 2019 IEEE.; E1
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 29th Australasian Universities Power Engineering Conference, AUPEC 2019
- Rights
- Copyright @ 2020 IEEE
- Rights
- This metadata is freely available under a CCO license
- Rights
- Open Access
- Subject
- Convolutional and residual neural networks; Dissipation and power factors; Neutralization number; Transformer oil age; Transformer oil image; Transformer oil quality
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