Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12258/14719
Title: An approach to neuro-fuzzy monitoring of power transformers
Authors: Koldaev, A. I.
Колдаев, А. И.
Evdokimov, A. A.
Евдокимов, А. А.
Shebzukhova, B. M.
Шебзухова, Б. М.
Keywords: Adaptive neuro-fuzzy systems;Dissolved gas analysis;Gas ratio;Monitoring;Power transformer;Fuzzy neural networks
Issue Date: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Koldaev, A.I., Evdokimov, A.A., Shebzukhova, B.M. An approach to neuro-fuzzy monitoring of power transformers // 2020 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2020. - 2020. - Номер статьи 9271394
Series/Report no.: 2020 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2020
Abstract: The article proposes a model of an adaptive network-based fuzzy inference system for power transformer monitoring based on the analysis of dissolved gases in transformer oil. The determination of the type and nature of the developing defect is carried out using the method of gas concentration ratios. The neuro-fuzzy system was tested on the results of dissolved gases analysis of power transformers operated at power plants. The proposed model of the neuro-fuzzy system with good accuracy allows you to determine the nature of the fault in the transformer. The proposed model of a neurofuzzy system can be used to build a continuous online monitoring system for power transformers
URI: http://hdl.handle.net/20.500.12258/14719
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

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