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首页> 外文期刊>Journal of energy and power engineering >Adaptive Neuro-Fuzzy Inference System for Thermal Field Evaluation of Underground Cable System
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Adaptive Neuro-Fuzzy Inference System for Thermal Field Evaluation of Underground Cable System

机译：地下电缆系统热场的自适应神经模糊推理系统

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摘要

The influence of thermal circuit parameters on a buried underground cable is investigated using an ANFIS (adaptive neuro-fuzzy inference system). Finite element solution of the heat conduction equation is used, combined with artificial intelligence methods. The cable temperature depends on several parameters, such as the ambient temperature, the currents flowing through the conductor and the resistivity of the surrounding soil. In this paper, ANFIS is used to simulate the problem of the thermal field of underground cables under various parameters variation and climatic conditions. The developed model was trained using data generated from FEM (finite element method) for different configurations (training set) of the thermal field problem. After training, the system is tested for several scenarios, differing significantly from the training cases. It is shown that the proposed method is very time efficient and accurate in calculating the thermal fields compared to the relatively time consuming finite element method; thus ANFIS provides a potential computationally efficient and inexpensive predictive tool for more effective thermal design of underground cable systems.
机译：使用ANFIS（自适应神经模糊推理系统）研究了热回路参数对地下地下电缆的影响。结合了人工智能方法，使用了热传导方程的有限元解。电缆温度取决于几个参数，例如环境温度，流经导体的电流以及周围土壤的电阻率。本文使用ANFIS来模拟各种参数变化和气候条件下地下电缆的热场问题。使用从FEM（有限元方法）生成的数据对热场问题的不同配置（训练集）进行训练。训练后，系统将针对几种情况进行测试，这与训练案例有很大不同。结果表明，与相对耗时的有限元方法相比，该方法在计算热场方面非常省时，准确。因此，ANFIS为更有效的地下电缆系统热设计提供了潜在的计算有效且廉价的预测工具。

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