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Evaluation of lightning performance of transmission lines protected by metal oxide surge arresters using artificial intelligence techniques

机译:使用人工智能技术评估由金属氧化物避雷器保护的传输线的防雷性能

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摘要

Lightning and switching overvoltages are the main causes for faults in electrical networks. In the last decades, several different conventional methodologies have been used for the adjustment of the lightning performance of high voltage transmission lines, which are protected against lightning using overhead ground wires and surge arresters. The current paper proposes a new developed Artificial Neural Network (ANN), based on the Q-learning algorithm, in order to estimate the lightning failure rate of lines of the Hellenic system. The results obtained by the ANN model exhibit a satisfactory correlation in comparison with the real recorded data or the simulations results taken from a conventional method.
机译:雷电和开关过电压是电网故障的主要原因。在过去的几十年中,已经使用了几种不同的常规方法来调整高压传输线的雷电性能,使用架空的接地线和电涌放电器可防止雷电。本文提出了一种基于Q学习算法的新开发的人工神经网络(ANN),以估计希腊系统线路的雷击失败率。通过ANN模型获得的结果与实际记录的数据或从常规方法获得的模拟结果相比显示出令人满意的相关性。

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