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- W2911591903 abstract "The hotspot temperature of electrical distribution transformers is usually taken into consideration to estimate its loss of life. In this article, we apply ambient temperature and load signal in an artificial neural network with the objective of estimating the top oil temperature of a transformer immersed in vegetable oil. It is used the IEEE Charging Guide to calculate the hotspot temperature based on this estimated top oil temperature. After that, we estimate the transformer’s loss of life. We repeat the tests, with the same transformer, using mineral oil. The architectures of the neural network used in this application are Multi-Layer Perceptron (MLP), Radial Basis Function (RBF) and Extreme Learning Machine (ELM). The variation of the resistance as a function of temperature is also considered. Simulation results are used to compare vegetable and mineral oil performance. Also, we analyze the neural network architectures. The results indicate the economic efficacy of the proposed methodology and that it is appropriated for the maintenance management of distribution transformers." @default.
- W2911591903 created "2019-02-21" @default.
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- W2911591903 date "2018-11-01" @default.
- W2911591903 modified "2023-10-01" @default.
- W2911591903 title "Artificial Neural Network Applied in Thermal Process of Distribution Transformers Immersed in Vegetable Oil" @default.
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- W2911591903 doi "https://doi.org/10.1109/induscon.2018.8627280" @default.
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