基于人工神经网络的传输线智能中继

Raghda Alilouch, F. Slaoui-Hasnaoui
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引用次数: 3

摘要

输电线路故障检测是电力系统监测与控制的重要组成部分。提供高质量的电力需要一个高效、可靠和智能的保护系统,一个能够处理由各种随机原因引起的输电线路中断的系统。该系统将允许快速检测并给出准确的故障位置,从而隔离故障部分,避免对物质和人力资产造成灾难性损害。本文提出了利用人工神经网络算法ANN在数值继电器中实现的方法,这种方法已受到电力系统保护领域许多研究者的注意。人工神经网络是通过测量三相电流和电压来训练的。将前馈神经网络与反向传播算法相结合,对故障进行检测、分类和定位。为了验证神经网络的选择,使用不同数量的隐藏层进行了详细的分析。仿真结果表明,基于人工神经网络的故障检测、分类和定位方法具有较好的效果。为了验证该方法,对不同的故障场景进行了仿真
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Intelligent Relay Based on Artificial Neural Networks ANN for Transmission Line
The detection of faults on transmission lines is an essential and important part of power system monitoring and control. Providing high-quality electric power requires an efficient, reliable, and intelligent protection, a system that can handle transmission line outages that result from a variety of random reasons. This system will allow a fast detection and gives an accurate fault location, thus isolating the faulted section and avoiding catastrophic damage to material and human assets.In this paper, the use of artificial neural network algorithm ANN is proposed, which can be implemented in a numerical relay, this approach has been noticed by many researchers in the field of power system protection. ANN is trained using the measurements of the three-phase currents and voltages. The feedforward neural network was used together with the backpropagation algorithm to detect, classify, and localize the fault. To validate the choice of the neural network, a detailed analysis was performed with a different number of hidden layers. Simulation results show that the present artificial neural network-based method performs satisfactorily in detecting, classifying, and locating faults on transmission lines. To test the proposed method, different fault scenarios were simulated
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