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首页> 外文会议>IEEE International Conference on Industry Applications >Artificial Neural Network Applied in Thermal Process of Distribution Transformers Immersed in Vegetable Oil
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Artificial Neural Network Applied in Thermal Process of Distribution Transformers Immersed in Vegetable Oil

机译：人工神经网络在浸入植物油配电变压器热过程中的应用

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摘要

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.
机译：通常考虑配电变压器的热点温度以估计其寿命损失。在本文中，我们在人工神经网络中应用环境温度和负载信号，目的是估计浸没在植物油中的变压器的最高油温。 IEEE充电指南将其用于根据此估计的最高机油温度来计算热点温度。之后，我们估计变压器的寿命。我们使用矿物油在同一台变压器上重复测试。此应用程序中使用的神经网络的体系结构是多层感知器（MLP），径向基函数（RBF）和极限学习机（ELM）。还考虑了电阻随温度的变化。仿真结果用于比较植物油和矿物油的性能。此外，我们分析了神经网络架构。结果表明了所提方法的经济有效性，适用于配电变压器的维护管理。

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