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Fault detection and classification technique in EHV transmission lines based on artificial neural networks

机译:基于人工神经网络的超高压输电线路故障检测与分类技术

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This paper investigates a new approach based on Artificial Neural Networks (ANNs) for real-time fault detection and classification in power transmission lines which can be used in digital power system protection. The Fault Detector and Classifier (FDC) consists of four independent ANNs. The technique uses consecutive magnitude current and voltage data at one terminal as inputs to the corresponding ANN. The ANN outputs are used to indicate simultaneously the presence and the type of the fault. The FDC is tested under different fault types, fault locations, fault resistances and fault inception angles. All test results show that the proposed FDC can be used for very high speed digital relaying.
机译:本文研究了一种基于人工神经网络(ANN)的电力传输线路实时故障检测和分类的新方法,该方法可用于数字电力系统保护。故障检测器和分类器(FDC)由四个独立的ANN组成。该技术使用一个端子处的连续幅度电流和电压数据作为相应ANN的输入。 ANN输出用于同时指示故障的存在和类型。 FDC在不同的故障类型,故障位置,故障电阻和故障起始角度下进行了测试。所有测试结果表明,所提出的FDC可以用于超高速数字中继。

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