Optimized Adaptive Overcurrent Protection Using Hybridized Nature-Inspired Algorithm and Clustering in Microgrids
Microgrids have been popularized in the past decade because of their ability to add distributed generation into the classic distribution systems. Protection problems are among several other problems that need solutions in order to extract the full capability of these novel networks. This research fo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/13/3324 |
| _version_ | 1797563835716993024 |
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| author | Thiramuni Sisitha Sameera Senarathna Kullappu Thantrige Manjula Udayanga Hemapala |
| author_facet | Thiramuni Sisitha Sameera Senarathna Kullappu Thantrige Manjula Udayanga Hemapala |
| author_sort | Thiramuni Sisitha Sameera Senarathna |
| collection | DOAJ |
| description | Microgrids have been popularized in the past decade because of their ability to add distributed generation into the classic distribution systems. Protection problems are among several other problems that need solutions in order to extract the full capability of these novel networks. This research follows the branches of two major solutions, namely adaptive protection and protection optimization. Adaptive protection implementation with a novel concept of clustering is considered, and protection setting optimization is done using a novel hybrid nature-inspired algorithm. Adaptive protection is utilized to cope with the topology variations, while optimization techniques are used to calculate the protection settings that provide faster fault clearances in coordination with backup protection. A modified IEEE 14 bus system is used as the test system. Validation was done for the fault clearing performance. The selected algorithm was effective than most other algorithms, and the clustering approach for adaptive overcurrent protection was able to reduce the number of relays’ setting groups. |
| first_indexed | 2024-03-10T18:48:49Z |
| format | Article |
| id | doaj.art-dd8e74921ec74c859caa0bc1399b1ad4 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T18:48:49Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-dd8e74921ec74c859caa0bc1399b1ad42023-11-20T05:21:42ZengMDPI AGEnergies1996-10732020-06-011313332410.3390/en13133324Optimized Adaptive Overcurrent Protection Using Hybridized Nature-Inspired Algorithm and Clustering in MicrogridsThiramuni Sisitha Sameera Senarathna0Kullappu Thantrige Manjula Udayanga Hemapala1Department of Electrical Engineering, University of Moratuwa, Moratuwa 10400, Sri LankaDepartment of Electrical Engineering, University of Moratuwa, Moratuwa 10400, Sri LankaMicrogrids have been popularized in the past decade because of their ability to add distributed generation into the classic distribution systems. Protection problems are among several other problems that need solutions in order to extract the full capability of these novel networks. This research follows the branches of two major solutions, namely adaptive protection and protection optimization. Adaptive protection implementation with a novel concept of clustering is considered, and protection setting optimization is done using a novel hybrid nature-inspired algorithm. Adaptive protection is utilized to cope with the topology variations, while optimization techniques are used to calculate the protection settings that provide faster fault clearances in coordination with backup protection. A modified IEEE 14 bus system is used as the test system. Validation was done for the fault clearing performance. The selected algorithm was effective than most other algorithms, and the clustering approach for adaptive overcurrent protection was able to reduce the number of relays’ setting groups.https://www.mdpi.com/1996-1073/13/13/3324adaptive protectionmicrogrid protectionprotection optimizationdirectional overcurrent protectionnature inspired optimization algorithmk-means clustering |
| spellingShingle | Thiramuni Sisitha Sameera Senarathna Kullappu Thantrige Manjula Udayanga Hemapala Optimized Adaptive Overcurrent Protection Using Hybridized Nature-Inspired Algorithm and Clustering in Microgrids Energies adaptive protection microgrid protection protection optimization directional overcurrent protection nature inspired optimization algorithm k-means clustering |
| title | Optimized Adaptive Overcurrent Protection Using Hybridized Nature-Inspired Algorithm and Clustering in Microgrids |
| title_full | Optimized Adaptive Overcurrent Protection Using Hybridized Nature-Inspired Algorithm and Clustering in Microgrids |
| title_fullStr | Optimized Adaptive Overcurrent Protection Using Hybridized Nature-Inspired Algorithm and Clustering in Microgrids |
| title_full_unstemmed | Optimized Adaptive Overcurrent Protection Using Hybridized Nature-Inspired Algorithm and Clustering in Microgrids |
| title_short | Optimized Adaptive Overcurrent Protection Using Hybridized Nature-Inspired Algorithm and Clustering in Microgrids |
| title_sort | optimized adaptive overcurrent protection using hybridized nature inspired algorithm and clustering in microgrids |
| topic | adaptive protection microgrid protection protection optimization directional overcurrent protection nature inspired optimization algorithm k-means clustering |
| url | https://www.mdpi.com/1996-1073/13/13/3324 |
| work_keys_str_mv | AT thiramunisisithasameerasenarathna optimizedadaptiveovercurrentprotectionusinghybridizednatureinspiredalgorithmandclusteringinmicrogrids AT kullapputhantrigemanjulaudayangahemapala optimizedadaptiveovercurrentprotectionusinghybridizednatureinspiredalgorithmandclusteringinmicrogrids |