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Location of faults generating short-duration voltage variations in distribution systems regions from records captured at one point and decomposed into damped sinusoids

机译:根据在一点捕获的记录并分解为阻尼正弦曲线的故障,在配电系统区域中产生短期电压变化的故障位置

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This study investigates the detection and localisation of faults provoking short-duration voltage variations ?? sags (dips) and swells ?? in small power distribution networks. It aims to accomplish those tasks by capturing fault records, voltage and current waveforms, at just one point in the system, the substation. The main objective is to classify the fault type and locate the fault origin occurring in a given region of a power delivery network. For that purpose, fault inception is triggered by a sensitive phase-locked loop. Then, the captured signals are decomposed using damped sinusoids of arbitrary temporal support by means of an adaptive decomposition algorithm. Subsets of the parameters defining the damped sinusoids are used for classifying the fault type and indicating the fault location. The fault-type classification is obtained by using support vector machines, whereas the fault location is obtained by means of an artificial neural network. The simulation results for a simple but actual power distribution system with three possible places for fault occurrence are presented. The exact fault-type classifications were obtained while a correct localisation of 85% of the faults was accomplished.
机译:这项研究调查了引起短时电压变化的故障的检测和定位。下垂(下垂)和膨胀??在小型配电网络中。它旨在通过捕获故障记录,电压和电流波形来完成这些任务,而该故障记录只是在变电站的系统中的一点。主要目的是对故障类型进行分类,并定位发生在输电网络给定区域中的故障根源。为此,通过敏感的锁相环来触发故障触发。然后,通过自适应分解算法,使用任意时间支持的阻尼正弦波对捕获的信号进行分解。定义阻尼正弦波的参数子集用于分类故障类型并指示故障位置。通过使用支持向量机获得故障类型的分类,而通过人工神经网络获得故障的位置。给出了一个简单但实​​际的配电系统的仿真结果,该系统具有三个可能发生故障的位置。获得了准确的断层类型分类,同时完成了85%的断层的正确定位。

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