# High-impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition
###### Abstract
This approach, which models HIFs as dynamic systems, allows for a more realistic representation of fault behavior, contributing to more accurate detection when compared to static models. The modeling technique helps in distinguishing HIFs from other types of faults by focusing on their unique electrical characteristics. In [27], a pre-trained convolutional neural network (CNN) was fine-tuned to detect various types of faults in photovoltaic (PV) arrays, including arcing faults. The HIF is modeled as a time-varying resistance and is included in the dataset along with the responses of other faults. This method leverages the CNNs' ability to automatically extract features from raw data, significantly improving detection accuracy compared to manually engineered features. Similarly, a CNN was used for HIF detection in [28]. However, a complex multistep training procedure involves training a generative adversarial network (GAN) before training the CNN. This procedure also requires a substantial dataset, which in this case is obtained from a PV emulator. While this method can enhance model robustness, the reliance on GANs and large datasets introduces additional complexity and may limit its applicability in resource-constrained environments. A similar approach is taken in [29], where the wavelet transform of current during different events is used as input to the RNN. The accuracy achieved is higher than that of the previous method. However, the dataset used for training is significantly larger, comprising almost one thousand HIF samples. This increase in dataset size contributes to improved performance but also presents challenges in terms of data acquisition and storage, which may not be feasible for all applications. In [30], the NN is used to detect arcing faults in PV systems. The accuracy of the method is significant; however, the dataset size is tens of thousands of samples, and obtaining such a dataset requires significant effort. This emphasizes the trade-off between model performance and the practical challenges associated with collecting large datasets, which is a common limitation in many machine learning applications. The need for a huge dataset size and HIF modeling also persists in [31], where the authors used different HIF models to create the dataset for training CNN-based fault detection. While this approach improves the model's ability to generalize, it underscores the importance of having a comprehensive and diverse dataset to achieve reliable results. This can be particularly challenging in fields like power grid monitoring, where fault events may be rare or difficult to simulate. The same principle is used in [32], where the authors employed different HIF models to create a dataset for fault detection in DC microgrids. The use of various fault models provides a more robust foundation for training the system, but it also increases the complexity of dataset generation and may introduce biases if not carefully controlled. Such approaches highlight the ongoing challenge of balancing model robustness with dataset representativeness in fault detection tasks. In [33], the authors went a step further by generating arc faults on a real DC system to create a dataset for their intelligent classification method. This real-world dataset helps bridge the gap between theoretical models and practical applications, ensuring that the system can handle real fault events effectively. However, the effort involved in generating real-world data is resource-intensive and may not be feasible in all cases, especially for large-scale systems. It is evident that intelligent fault detection methods heavily rely on HIF models in their datasets, requiring significant effort from the authors to accurately model HIFs in both simulations and real-world environments [34; 35]. Hence, it is desirable to reduce or even completely eliminate the classifier's dependence on HIF modeling. One way to achieve this is by training the classifier to learn patterns that occur during normal operating conditions.