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Improved Monitoring and Diagnosis of Transformer Solid Insulation Using Pertinent Chemical Indicators
- Source :
- Energies, Vol 14, Iss 3977, p 3977 (2021), Energies; Volume 14; Issue 13; Pages: 3977
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- Transformers are generally considered to be the costliest assets in a power network. The lifetime of a transformer is mainly attributable to the condition of its solid insulation, which in turn is measured and described according to the degree of polymerization (DP) of the cellulose. Since the determination of the DP index is complex and time-consuming and requires the transformer to be taken out of service, utilities prefer indirect and non-invasive methods of determining the DP based on the byproduct of cellulose aging. This paper analyzes solid insulation degradation by measuring the furan concentration, recently introduced methanol, and dissolved gases like carbon oxides and hydrogen, in the insulating oil. A group of service-aged distribution transformers were selected for practical investigation based on oil samples and different kinds of tests. Based on the maintenance and planning strategy of the power utility and a weighted combination of measured chemical indicators, a neural network was also developed to categorize the state of the transformer in certain classes. The method proved to be able to improve the diagnostic capability of chemical indicators, thus providing power utilities with more reliable maintenance tools and avoiding catastrophic failure of transformers.
- Subjects :
- transformer
condition assessment
degradation
furan
methanol
multi-layer perceptron (MLP)
Power utility
Technology
Control and Optimization
Transformer oil
020209 energy
Energy Engineering and Power Technology
02 engineering and technology
Distribution transformer
01 natural sciences
0103 physical sciences
0202 electrical engineering, electronic engineering, information engineering
Electrical and Electronic Engineering
Process engineering
Engineering (miscellaneous)
Transformer (machine learning model)
010302 applied physics
Renewable Energy, Sustainability and the Environment
business.industry
Solid insulation
Power (physics)
Carbon oxide
Catastrophic failure
Environmental science
business
Energy (miscellaneous)
Subjects
Details
- Language :
- English
- ISSN :
- 19961073
- Volume :
- 14
- Issue :
- 3977
- Database :
- OpenAIRE
- Journal :
- Energies
- Accession number :
- edsair.doi.dedup.....5561d48ab39bfe4905b015df78051fd0