# High-impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition
###### Abstract
The performance is evaluated based on the probability of detection and the false alarm ratio, providing insights into the effectiveness of these novel methods; however, they have yet to be applied to DC systems. In [18], the HIF detection is performed by injecting high-frequency oscillation at fault inception. However, the latter methods do not take into account the arcing nature of the HIF. In [19], the authors modeled the arcing nature of HIF and used power spectral characteristic along with the current sag to detect them. They argued that a precise HIF model is crucial for the development of fault detection methods. Additionally, they noted that modeling is difficult due to the dependence of fault responses on operating conditions. The same is confirmed in [20], where it is noted that HIFs typically occur when a line comes into contact with a high-impedance surface, resulting in low-current arcs. These arcs occur intermittently and at random intervals because the contact area is not constant, posing a significant challenge for HIF modeling. Although an HIF model cannot be asserted owing to its random properties, certain insights can be derived from the differences in data recorded during these failures and data recorded during normal operation. This can be achieved by applying machine learning models to find patterns or clusters or classify the data [21]. Several works have used machine learning models to distinguish an HIF from normal operation. In [22], the authors implemented an accurate HIF model with arcing, created a dataset and trained a k-nearest neighbors (kNN) classifier to detect HIFs. The dataset contains the difference in coefficient energy between the normal and faulty states obtained by the discrete wavelet transform (DWT) of the current signal and non-faulty samples. However, kNN is sensitive to noisy data, as is the case with HIF, and when the dimensionality of the data increases, its performance decreases. In [23], DWT was also used to pre-process the current measurement, but the support vector machine (SVM) classifier is used to detect HIFs. However, the arcing phenomenon is not considered when modeling HIFs and SVM is prone to problems similar to those of kNN. Neural networks (NNs) offer certain advantages over classical machine learning algorithms such as kNN and SVM in terms of susceptibility to noise and the ability to work with high-dimensional data [24]. This is particularly advantageous for high-impedance fault (HIF) recognition because NNs can effectively process and classify the varying waveforms and patterns in power signals that traditional algorithms may struggle with. Furthermore, NN models are more adaptable, making them capable of handling data with complex, nonlinear relationships, which is a common characteristic in real-world fault detection scenarios. In [25], an NN is used in the process of HIF detection to predict the behavior of the power signal, but it is not used directly for classification. While this approach can successfully predict trends in the data, it lacks the ability to make definitive classification decisions, thus limiting its practical application in real-time fault detection. The method's reliance on predicting signal behavior highlights the importance of accurately modeling fault dynamics for future improvements. On the other hand, in [26], a recurrent neural network (RNN) was used to detect HIFs. The method uses a current measurement to detect HIFs in a radial DC microgrid. The RNN is used because its ability to work with time series has several advantages, such as taking into account previously seen samples without the need for fixed time windows. This temporal context enables the model to capture long-term dependencies and variations in the data, which is especially useful for dynamic fault detection in real-time environments. Furthermore, the RNN's structure is well suited to model the evolving nature of fault signals over time. HIFs are modeled as a nonlinear resistances that resemble fault current reignition.