DC Arc-Fault Detection Based on Empirical Mode Decomposition of Arc Signatures and Support Vector Machine
DC Arc-Fault Detection Based on Empirical Mode Decomposition of Arc Signatures and Support Vector Machine
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DOI：
10.1109/jsen.2020.3041737
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发表时间：
2021-03
影响因子：
4.3
通讯作者：
Wenchao Miao;Qi Xu;K. Lam;P. Pong;Senior Member;H. Poor;Life Fellow
中科院分区：
文献类型：
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作者：
Wenchao Miao;Qi Xu;K. Lam;P. Pong;Senior Member;H. Poor;Life Fellow
保护装置广泛应用于直流系统中，以保证直流系统的正常运行和安全。然而，在导体之间的空气中建立电流路径的串联电弧故障将电弧阻抗引入系统。因此，它们可能导致电流的减小，并且因此传统的保护装置可能不会被触发。未检测到的串联电弧故障可能导致故障，甚至导致火灾。因此，串联电弧故障检测系统对直流系统的可靠、高效运行至关重要。提出了一种基于改进的经验模式分解（EMD）技术提取电弧时频特征，并采用支持向量机（SVM）算法进行决策的直流系统串联电弧故障检测系统。通过EMD分解电弧电流的振荡频率，并分析Hurst指数（${H}$），以排除电力电子噪声的干扰。${H}$分析信号的趋势，信号的固有振荡是指${H}$值大于1/2的振荡。与传统的滤波器或小波变换相比，该方法不需要知道干扰的频率范围，而干扰的频率范围因系统而异。所提出的技术的能力和适用性在光伏系统中进行了验证。该方法可以获得足够的、准确的电弧特征，并且不需要设置各种阈值，从而显著提高了电弧故障检测的有效性。
Protection devices are extensively utilized in direct current (DC) systems to ensure their normal operation and safety. However, series arc faults that establish current paths in the air between conductors introduce arc impedance to the system. Consequently, they can result in a decrease of current, and thus conventional protection devices may not be triggered. Undetected series arc faults can cause malfunctions and even lead to fire hazards. Therefore, a series arc-fault detection system is essential to DC systems to operate reliably and efficiently. In this paper, a series arc-fault detection system based on arc time-frequency signatures extracted by a modified empirical mode decomposition (EMD) technique and using a support vector machine (SVM) algorithm in decision making is proposed for DC systems. The oscillatory frequencies from the arc current are decomposed by the EMD with an analysis of the Hurst exponent ( ${H}$ ) to reject interference from the power electronics noise. ${H}$ analyzes the trend of a signal and the intrinsic oscillations of the signal are those with values of ${H}$ larger than 1/2. Comparing to traditional filters or wavelet transforms, this method does not require knowledge of the frequency range of the interference which varies from system to system. The capability and applicability of the proposed technique are validated in a photovoltaic system. The effectiveness of arc-fault detection is significantly improved by this technique because it can acquire sufficient and accurate arc signatures and it does not need to predefine various thresholds.