High-Impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition

微电网 断层(地质) 计算机科学 电气工程 工程类 电压 地质学 地震学
作者
Ivan Grcić,Hrvoje Pandžić
出处
期刊:Applied sciences [Multidisciplinary Digital Publishing Institute]
卷期号:15 (1): 193-193
标识
DOI:10.3390/app15010193
摘要

Detection of high-impedance faults in direct current microgrid lines presents a challenge for most conventional protection schemes because the magnitude of the fault current is similar to other transients that occur during normal operation. However, the waveform of high-impedance faults differs from that of other transients as it is characterized by a repetitive and nonlinear pattern caused by current reignition. Various methods have been proposed to exploit fault response waveforms for detecting high-impedance faults, including those based on deep discriminative intelligent classification. Different from previous works that focus on closed-set classification, this study frames fault detection as an open-set recognition problem, employing a neural network as the classifier. The resulting approach enables the detection of high-impedance faults as outliers from the normal operating states of microgrid lines with passive constant impedance loads and requires only the Fourier transform of the current signal as input to the neural network. Remarkably, the proposed solution eliminates the need for hard-to-model high-impedance faults in the training dataset and hence is more generally applicable. The proposed method consistently outperforms commercially available high-impedance fault detection systems, achieving high accuracy in fault detection.
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