High-Impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition
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...
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MDPI AG
2024-12-01
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| author | Ivan Grcić Hrvoje Pandžić |
| author_facet | Ivan Grcić Hrvoje Pandžić |
| author_sort | Ivan Grcić |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ff3a81b67b2b4816850fa5824c05401f |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-ff3a81b67b2b4816850fa5824c05401f2025-01-10T13:14:45ZengMDPI AGApplied Sciences2076-34172024-12-0115119310.3390/app15010193High-Impedance Fault Detection in DC Microgrid Lines Using Open-Set RecognitionIvan Grcić0Hrvoje Pandžić1Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, CroatiaFaculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, CroatiaDetection 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.https://www.mdpi.com/2076-3417/15/1/193fault detectionfault protectionFourier transformmachine learningmicrogridsneural networks |
| spellingShingle | Ivan Grcić Hrvoje Pandžić High-Impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition Applied Sciences fault detection fault protection Fourier transform machine learning microgrids neural networks |
| title | High-Impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition |
| title_full | High-Impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition |
| title_fullStr | High-Impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition |
| title_full_unstemmed | High-Impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition |
| title_short | High-Impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition |
| title_sort | high impedance fault detection in dc microgrid lines using open set recognition |
| topic | fault detection fault protection Fourier transform machine learning microgrids neural networks |
| url | https://www.mdpi.com/2076-3417/15/1/193 |
| work_keys_str_mv | AT ivangrcic highimpedancefaultdetectionindcmicrogridlinesusingopensetrecognition AT hrvojepandzic highimpedancefaultdetectionindcmicrogridlinesusingopensetrecognition |