An Advanced Local Current-Only Protection for Microgrids: Deep-Learning-Based Approach
The integrity of protection actions in microgrids (MGs) is often compromised by varying operational conditions and contingencies. Notably, the operation of on-load tap-changers (OLTCs) can lead to the maloperation of overcurrent relays during both internal and external faults due to the altered faul...
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IEEE
2025-01-01
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| author | Amin Yazdaninejadi Hossein Ebrahimi Sajjad Golshannavaz |
| author_facet | Amin Yazdaninejadi Hossein Ebrahimi Sajjad Golshannavaz |
| author_sort | Amin Yazdaninejadi |
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| description | The integrity of protection actions in microgrids (MGs) is often compromised by varying operational conditions and contingencies. Notably, the operation of on-load tap-changers (OLTCs) can lead to the maloperation of overcurrent relays during both internal and external faults due to the altered fault current levels. This issue, combined with diverse operational conditions and other N-1 contingencies, further broadens the scope of undesirable operations. A deep-learning (DL) assisted fault detection method is introduced in this paper, which addresses the impact of OLTC operation on overcurrent protection in MGs under different operational scenarios. The proposed method utilizes a long short-term memory (LSTM) model to detect faults, taking into account the OLTC operation, on/off-grid status of the MG, outages of distributed generations (DGs), and capacitor bank switching. Additionally, various fault locations and resistances are incorporated into all simulated fault scenarios to enhance the training performance of the method. The ability of the LSTM model to interpret temporal dependencies in time-series signals allows the proposed method to rely solely on single-sourced current measurements at the relaying point, thereby reducing costs and improving module security. Simulation results on the low-voltage section of the IEEE 14-bus test system demonstrate the effectiveness of the proposed method in fault detection compared to existing approaches. |
| format | Article |
| id | doaj-art-19fb2f19514f474bbc950215fe59c9df |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-19fb2f19514f474bbc950215fe59c9df2025-08-20T01:51:39ZengIEEEIEEE Access2169-35362025-01-0113564285643810.1109/ACCESS.2025.355512310942361An Advanced Local Current-Only Protection for Microgrids: Deep-Learning-Based ApproachAmin Yazdaninejadi0https://orcid.org/0000-0003-4506-2988Hossein Ebrahimi1https://orcid.org/0000-0003-3872-0786Sajjad Golshannavaz2https://orcid.org/0000-0003-4999-8281Department of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, IranElectrical Engineering Department, Urmia University, Urmia, IranElectrical Engineering Department, Urmia University, Urmia, IranThe integrity of protection actions in microgrids (MGs) is often compromised by varying operational conditions and contingencies. Notably, the operation of on-load tap-changers (OLTCs) can lead to the maloperation of overcurrent relays during both internal and external faults due to the altered fault current levels. This issue, combined with diverse operational conditions and other N-1 contingencies, further broadens the scope of undesirable operations. A deep-learning (DL) assisted fault detection method is introduced in this paper, which addresses the impact of OLTC operation on overcurrent protection in MGs under different operational scenarios. The proposed method utilizes a long short-term memory (LSTM) model to detect faults, taking into account the OLTC operation, on/off-grid status of the MG, outages of distributed generations (DGs), and capacitor bank switching. Additionally, various fault locations and resistances are incorporated into all simulated fault scenarios to enhance the training performance of the method. The ability of the LSTM model to interpret temporal dependencies in time-series signals allows the proposed method to rely solely on single-sourced current measurements at the relaying point, thereby reducing costs and improving module security. Simulation results on the low-voltage section of the IEEE 14-bus test system demonstrate the effectiveness of the proposed method in fault detection compared to existing approaches.https://ieeexplore.ieee.org/document/10942361/Microgrid protectionfault detection strategyoperational conditionslong short-term memory (LSTM)overcurrent relays characteristics |
| spellingShingle | Amin Yazdaninejadi Hossein Ebrahimi Sajjad Golshannavaz An Advanced Local Current-Only Protection for Microgrids: Deep-Learning-Based Approach IEEE Access Microgrid protection fault detection strategy operational conditions long short-term memory (LSTM) overcurrent relays characteristics |
| title | An Advanced Local Current-Only Protection for Microgrids: Deep-Learning-Based Approach |
| title_full | An Advanced Local Current-Only Protection for Microgrids: Deep-Learning-Based Approach |
| title_fullStr | An Advanced Local Current-Only Protection for Microgrids: Deep-Learning-Based Approach |
| title_full_unstemmed | An Advanced Local Current-Only Protection for Microgrids: Deep-Learning-Based Approach |
| title_short | An Advanced Local Current-Only Protection for Microgrids: Deep-Learning-Based Approach |
| title_sort | advanced local current only protection for microgrids deep learning based approach |
| topic | Microgrid protection fault detection strategy operational conditions long short-term memory (LSTM) overcurrent relays characteristics |
| url | https://ieeexplore.ieee.org/document/10942361/ |
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