Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 17, Issue 7, Pages 4624-4634Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.3014224
Keywords
Moisture; Support vector machines; Oil insulation; Power transformer insulation; Dielectrics; Frequency-domain spectroscopy (FDS); genetic algorithm support vector machine (GA-SVM); moisture diagnosis; oil-immersed insulation; power transformer
Categories
Funding
- Basic Ability Improvement Project for Young and Middle-Aged Teachers in Universities of Guangxi [20190046, 20190067]
- Brunel Research Initiative and Enterprise Fund
- Education Department of Guangdong Province [2016KCXTD022]
- National Natural Science Foundation of China [51867003, 61473272]
- Natural Science Foundation of Guangxi Province [2018JJA160176, 2018JJB160064]
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This article introduces a new intelligent model for moisture diagnosis in transformers, which utilizes a genetic algorithm support vector machine (GA-SVM) and obtains feature parameters through frequency-domain spectroscopy. The feasibility and accuracy of this model are demonstrated in lab and field conditions.
Moisture is one of the critical factors to determine the service life of transformers. The moisture inside the transformer oil-immersed insulation could be quantified with feature parameters. This article proposes and develops a genetic algorithm support vector machine (GA-SVM) model to carry out the moisture diagnosis. Present findings reveal that these feature parameters can be obtained by using frequency-domain spectroscopy. Therefore, a novel model for predicting the frequency-domain spectroscopy curves is first reported based on a small number of samples, which could be utilized to obtain the feature parameters database to develop GA-SVM. Then, the moisture diagnosis in the lab and field conditions is presented to verify its feasibility and accuracy. The novelty of this article is in an exploration of the reported model as an intelligent based moisture diagnosis tool for power transformers.
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