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首页> 外文期刊>Electric power systems research >A new FDOST entropy based intelligent digital relaying for detection, classification and localization of faults on the hybrid transmission line
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A new FDOST entropy based intelligent digital relaying for detection, classification and localization of faults on the hybrid transmission line

机译：一种基于FDOST熵的新型智能数字中继，用于混合传输线上的故障检测，分类和定位

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The paper presents a new digital relaying for detection, classification and localization of faults on the hybrid transmission line consisting of an overhead line and an underground cable. The entropy principle together with fast discrete orthogonal S-transform (FDOST) represented by window dependent bases is utilized for feature extraction and the support vector machine (SVM) classifier model & support vector regression (SVR) model are employed for pattern recognitions to predict the types and locations of faults. After modelling and simulation of the transmission system in Electromagnetic Transient Program (EMTP) software, three phase fault current signals are recorded at one end of the line to extract entropy of FDOST coefficients from each of the three current signals of half cycle duration after fault initiation. The proposed relaying technique is tested on a single-junction and a multi junction hybrid transmission lines under different fault conditions and is found fast and accurate independent of fault type, fault section, fault resistance, fault inception angle (FIA) and load angle. Another important aspect of the method is that it needs no prior identification of the faulty section for the estimation of fault location. The immunity of the proposed method to noise is also established by testing it with fault current signals impregnated with white Gaussian noise of level 30 dB signal to noise ratio (SNR). (C) 2017 Elsevier B.V. All rights reserved.
机译：本文提出了一种新的数字继电器，用于在架空线和地下电缆组成的混合传输线上进行故障的检测，分类和定位。熵原理与依赖于窗口的基数表示的快速离散正交S变换（FDOST）一起用于特征提取，支持向量机（SVM）分类器模型和支持向量回归（SVR）模型用于模式识别以预测故障的类型和位置。在使用电磁暂态程序（EMTP）软件对传输系统进行建模和仿真之后，在线路的一端记录了三相故障电流信号，以从故障启动后半周期持续时间的三个电流信号中的每一个中提取FDOST系数的熵。 。所提出的中继技术在不同故障条件下的单结和多结混合传输线上进行了测试，并且能够快速，准确地发现故障类型，故障截面，故障电阻，故障起始角（FIA）和负载角。该方法的另一个重要方面是，它不需要为确定故障位置而预先确定故障部分。还通过用故障电流信号测试该方法对噪声的抗扰性，该故障电流信号中充满了电平为30 dB的信噪比（SNR）的高斯白噪声。 （C）2017 Elsevier B.V.保留所有权利。

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