# High-impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition
###### Abstract
Then, any deviation from these patterns is marked as an anomaly, or in this case, a fault. This approach is especially convenient for parts of the microgrid where the patterns of normal operating states are not diverse, as microgrid can have different topologies and operate in various states. However, the conventional training procedure for NNs does not enable this. Fortunately, there are specific methods for training NNs that allow them to detect outliers or anomalies. The problem of detecting outliers using machine learning models is called the open-set recognition (OSR) problem, formalized in [36], where the authors discussed how far the sample should be from the decision boundary to be considered an outlier. The same principle applies when employing NNs for classification [37]. Accordingly, various approaches have been proposed to enhance the OSR capabilities of NNs. These include methods that introduce different algorithms, metrics, outlier rejection rules, and modifications to the training procedure [38]. This work focuses on the latter, as it provides a straightforward and explainable procedure, which is introduced in Section 3. Lines connecting the passive loads to the microgrid buses are typically unaffected by the microgrid's operating states, as their current is determined by the load impedance. Additionally, the number of events is minimal, only consisting of stationary states and load change events, simplifying the task of capturing all normal states. Given that there are methods allowing NNs to detect outliers, this paper proposes using that capability to identify HIFs in microgrid lines with passive loads as outliers from normal operating conditions. In this approach, the dataset contains only normal operating states, such as a stationary states or load changes. Since HIFs can be detected as outliers, there is no need to include them in the dataset, and more significantly, to model them. The benefits of this approach are manifold: (i) it eliminates the need of HIF modeling; (ii) the fault detection method is not entirely reliant on the selected HIF model; and (iii) detection is agnostic of the operating conditions under which an HIF occurs. The contribution of this paper is summarized as follows:  
* Modeling high-impedance fault detection as an open-set recognition problem;
* Development of a novel high-impedance fault detection method in DC microgrids based on neural network with open-set recognition capabilities;
* Simulation and detailed analysis of high-impedance faults in DC microgrid lines connecting passive loads. The remainder of this paper is organized as follows. Section 2 provides a description of common types of faults that occur in microgrids, specifically low-impedance and high-impedance faults. Section 3 discusses the application of neural networks for classification, along with the OSR problem and its impact on classification performance. Furthermore, the proposed fault detection method is presented, emphasizing the utilization of the OSR framework for neural networks in fault detection. In Section 4, a case study demonstrating the effectiveness of the proposed method is presented. The DC microgrid model and dataset used in the study are described, and the parameters of the neural networks employed for detection are provided together with the results. Additionally, the algorithm used for the fault detection is outlined. Finally, Section 5 concludes the paper and summarizes the key findings and contributions of this research.