期刊
IEEE TRANSACTIONS ON POWER DELIVERY
卷 31, 期 4, 页码 1778-1779出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRD.2016.2521320
关键词
Artificial intelligent; furan; health index; transformer
资金
- Qatar National Research Fund (a member of Qatar Foundation) [NPRP 5-044-2-016]
In this letter, the ranges of furan content in oil in power transformers are predicted using measurements of oil tests, such as breakdown voltage, acidity, water content, and dissolved gas analysis. Predictive models based on machine-learning techniques are trained and tested to estimate the furan level. A prediction accuracy of 90% is achieved when using k-nearest neighbors as the classification model with a wrapper method as the feature selection technique.
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