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首页> 外文期刊>Electric power systems research >Application of artificial neural network methods for the lightning performance evaluation of Hellenic high voltage transmission lines
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Application of artificial neural network methods for the lightning performance evaluation of Hellenic high voltage transmission lines

机译：人工神经网络方法在希腊高压输电线路雷电性能评估中的应用

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摘要

Feed-forward (FF) artificial neural networks (ANN) and radial basis function (RBF) ANN methods were addressed for evaluating the lightning performance of high voltage transmission lines. Several structures, learning algorithms and transfer functions were tested in order to produce a model with the best generalizing ability. Actual input and output data, collected from operating Hellenic high voltage transmission lines, as well as simulated output data were used in the training, validation and testing process. The aims of the paper are to describe in detail and compare the proposed FF and RBF ANN models, to state their advantages and disadvantages and to present results obtained by their application on operating Hellenic transmission lines of 150 kV and 400 kV. The ANN results are also compared with results obtained using conventional methods and real records of outage rate showing a quite satisfactory agreement. The proposed ANN methods can be used by electric power utilities as useful tools for the design of electric power systems, alternative to the conventional analytical methods.
机译：提出了前馈（FF）人工神经网络（ANN）和径向基函数（RBF）ANN方法，用于评估高压输电线路的雷电性能。测试了几种结构，学习算法和传递函数，以便生成具有最佳泛化能力的模型。从运行中的希腊高压输电线路收集的实际输入和输出数据，以及模拟的输出数据都用于培训，验证和测试过程。本文的目的是详细描述和比较所提出的FF和RBF神经网络模型，以陈述它们的优缺点，并介绍它们在150 kV和400 kV希腊输电线路上的应用所获得的结果。人工神经网络的结果也与使用常规方法获得的结果进行了比较，并且实际中断率记录显示出令人满意的一致性。拟议的人工神经网络方法可以代替传统的分析方法，由电力公司用作设计电力系统的有用工具。

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