# High-impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition
###### Abstract  
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. fault detection; fault protection; Fourier transform; machine learning; microgrids; neural networks +
Footnote †: journal: Applied sciences  
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High-impedance faults (HIFs) present such a problem, as they cannot be detected by conventional system protection methods. However, the importance of HIF detection in DC systems is continuously stressed, as these systems are now prevalent in electric vehicles, photovoltaic plants, data centers, high-voltage DC transmission, and DC microgrids [9]. The only conventional system protection able to detect HIFs is differential protection, which has already been used for fault detection in cables in DC microgrids [10]. The method proved to be very effective; however, the differential protection is too expensive to implement on each microgrid line. In addition, the communication link is critical, as its failure will cause the protection not to work. It is also worth noting that other commercially available solutions developed specifically for HIF detection exhibit a relatively low successful action rate, with only 61.67% of the faults detected [11]. Various approaches have been adopted to solve HIF detection in DC microgrids. The authors in [12] observed incremental resistance as seen from the converter to detect an HIF. The method is simple, but how it behaves with different fault resistances is not investigated. The same problem arises with the method proposed in [13], where the impedance is predicted using an autoregressive model. In [14], the ground current is measured and when it reaches a certain threshold the operator is warned or, in case of a severe fault, the circuit breaker is tripped. In [15], the fault detection procedure is started when the current derivative exceeds a predefined value. In the next step, the resistance at both ends of the line segment is estimated using the least squares method. The sign of the resistances is then used to detect a fault and determine whether it is an external fault. An interesting approach for detecting partial discharge based on methods such as compressive sensing and phase diagrams is also presented [16; 17]. These papers focus on two recent advancements in partial discharge detection: compressive sensing and phase-diagram-based analysis. These techniques are compared using real signals from an experimental setup designed to simulate realistic conditions, including load signals, reflections and propagation effects.