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Series Arc Fault Detection in Photovoltaic Systems Based on Signal-to-Noise Ratio Characteristics Using Cross-Correlation Function

机译:基于信号 - 信噪比使用互相关功能的光伏系统系列电弧故障检测

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摘要

Series arc fault (SAF) is one of the most important causes for failure of photovoltaic (PV) systems. Conventional protections could not detect SAF at initial moments. In this paper, a new method is presented for detection of SAF. The method is based on extracting SAF signatures from high frequency contents of the normalized dc terminal voltage. Inverter switching signatures in the signal have a periodic nature, which are rejected by subtracting the resized data-windows based on the lag relevant to maximum cross-correlation value. The utilized criterion is defined as ratio of power of the low frequency components to power of the arc signal (or signal-to-noise ratio). A set of possible scenarios, whether in practice or simulation, is considered to evaluate the performance of the method. They include different arc lengths, different fault locations in the PV system, various partial shadings, different switching frequencies for the inverter, and different types of environmental noise.
机译:系列弧形故障(SAF)是光伏(PV)系统失效最重要的原因之一。传统保护无法在初始时刻检测SAF。本文提出了一种用于检测SAF的新方法。该方法基于从归一化DC端电压的高频内容提取SAF签名。信号中的逆变器切换签名具有周期性,通过基于与最大互相关值相关的滞后减去调整大小的数据窗口来拒绝。利用标准定义为低频分量与电弧信号(或信噪比)的功率的功率的比率。无论是在实践还是模拟,都被认为是评估方法的性能。它们包括不同的电弧长度,PV系统中的不同故障位置,各种部分阴影,逆变器的不同开关频率,以及不同类型的环境噪声。

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