# High-impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition
## 4 Case Study
### Discussion  
The goal of this study is to develop an HIF detection method that is based on an NN trained according to the OSR suggestions. These suggestions allow unknowns (inputs that are not seen by the classifier during its training--in this case HIFs) to be treated as outliers. The first advantage of this approach is that the HIF responses do not need to be included in the dataset, which relieves the user of the need to model HIFs and reduces complexity of the classifier (NN). This is achieved because both the FFNN and the CNN classifiers have simple architectures but can still properly handle unknowns, making it possible to set bounds around the correctly classified samples. Consequently, the detection of outliers (HIFs) is simple--it is enough to check whether the MLS of an input is outside the bounds of the class assigned by the classifier. It should be noted that it cannot be known a priori which class an input will be assigned to, so the MLS is compared to the bounds of the class into which the classifier has placed it. This is the general case, but it may happen that the same bounds can be applied to both classes, so it is not necessary to know which class the classifier has assigned to the input. The bounds do not have to be fixed, as any strategy can be used to detect outliers. In this work, the fixed bound is used for simplicity.  
Speaking of disturbances other than HIFs, some characteristics could be included in the dataset, but it is difficult to know and model all the disturbances that occur in a microgrid. It is assumed here that the HIF response is specific and different from that of other disturbances. Moreover, 100 ms is long enough that temporary disturbances do not trigger the trip signal, while HIFs, on the other hand, persist. At the time of writing, the authors are not aware that a dataset for HIF detection exists. Since the dataset is publicly available, it can be used as a basis for further research. Any improvements to the dataset and method are encouraged. Future work can be based on any of the above: enhancing the dataset by including more normal operating points or adding real-world HIF responses, improving the classifiers or the training procedure, or treating outliers differently. Testing the method on real-world data would increase pressure on classifier's discriminative capabilities, potentially raising the need for input filtering, using different signal decomposition methods (e.g., wavelet transform), or incorporation of additional input features. One of the research directions could cover adaptation of the proposed method for lines that connect power sources, energy storage systems or active loads, as it is currently limited to lines connecting passive loads. Another research direction could investigate applying the method to long DC lines, such as those used in HVDC transmission systems.  
It is also worth noting that hardware could be a limiting factor, as the method requires implementation of the neural network on the processor. This involves handling fast matrix multiplication and implementation of nonlinear functions, which must be carefully managed to minimize computational overhead and ensure fast execution. Both factors impact the neural network's classification performance, which, in turn, directly affects the fault detection performance [59; 60].