OPTIMAL DISTRIBUTION NETWORK RECONFIGURATION FOR LOSS MINIMIZATION AND VOLTAGE PROFILE IMPROVEMENT BASED ON ARTIFICIAL INTELLIGENCE
Optimal reconfiguration is a significant alternative technique of increasing the efficacy of Radial Distribution Networks (RDNs). Reconfiguration is carried out by adjusting the status of RDN switches in such manner that the system's radiality is kept, energized wholly loads and other restricti...
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Faculty of Engineering, University of Kufa
2025-04-01
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| Series: | Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ |
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| Online Access: | https://journal.uokufa.edu.iq/index.php/kje/article/view/16197 |
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| author | Omar Muhammed Neda |
| author_facet | Omar Muhammed Neda |
| author_sort | Omar Muhammed Neda |
| collection | DOAJ |
| description | Optimal reconfiguration is a significant alternative technique of increasing the efficacy of Radial Distribution Networks (RDNs). Reconfiguration is carried out by adjusting the status of RDN switches in such manner that the system's radiality is kept, energized wholly loads and other restrictions are fulfilled. The original version of the Dolphin Echolocation Optimization (DEO) algorithm is designed for solving continuous optimization issues only. As the reconfiguration problem is a discrete issue, the original DEO algorithm cannot deal with this problem. Fortunately, a Binary DEO (BDEO) algorithm was presented for solving discrete optimization issues which is utilized for adapting the reconfiguration issue. This approach is a powerful tool for rearranging systems by altering the status of the RDN switches in a way that minimizes power loss and enhances voltage profile. The BDEO algorithm is evaluated on an IEEE 33 bus RDN under three case studies in MATLAB to validate its performance. By comparing the simulation results with those from previously published work, it is possible to conclude that the suggested strategy is efficient in achieving the optimal outcome because it enhances the system voltage profile while minimizing losses. The comparison results showed that, in instance two, the BDEO for the test RDN greatly increased the minimum voltage from 0.9131 to 0.9431 P.U. and reduced the power loss by 34.2%, from 202.67 to 133.17 KW. |
| format | Article |
| id | doaj-art-46a0b0eea7ed4a2eb737fd0e7e2591ac |
| institution | DOAJ |
| issn | 2071-5528 2523-0018 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Faculty of Engineering, University of Kufa |
| record_format | Article |
| series | Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ |
| spelling | doaj-art-46a0b0eea7ed4a2eb737fd0e7e2591ac2025-08-20T03:11:54ZengFaculty of Engineering, University of KufaMağallaẗ Al-kūfaẗ Al-handasiyyaẗ2071-55282523-00182025-04-01160226327910.30572/2018/KJE/160216OPTIMAL DISTRIBUTION NETWORK RECONFIGURATION FOR LOSS MINIMIZATION AND VOLTAGE PROFILE IMPROVEMENT BASED ON ARTIFICIAL INTELLIGENCEOmar Muhammed Neda0https://orcid.org/0000-0001-9748-3341Department of Electrical Engineering, Sunni Diwan Endowment, IraqOptimal reconfiguration is a significant alternative technique of increasing the efficacy of Radial Distribution Networks (RDNs). Reconfiguration is carried out by adjusting the status of RDN switches in such manner that the system's radiality is kept, energized wholly loads and other restrictions are fulfilled. The original version of the Dolphin Echolocation Optimization (DEO) algorithm is designed for solving continuous optimization issues only. As the reconfiguration problem is a discrete issue, the original DEO algorithm cannot deal with this problem. Fortunately, a Binary DEO (BDEO) algorithm was presented for solving discrete optimization issues which is utilized for adapting the reconfiguration issue. This approach is a powerful tool for rearranging systems by altering the status of the RDN switches in a way that minimizes power loss and enhances voltage profile. The BDEO algorithm is evaluated on an IEEE 33 bus RDN under three case studies in MATLAB to validate its performance. By comparing the simulation results with those from previously published work, it is possible to conclude that the suggested strategy is efficient in achieving the optimal outcome because it enhances the system voltage profile while minimizing losses. The comparison results showed that, in instance two, the BDEO for the test RDN greatly increased the minimum voltage from 0.9131 to 0.9431 P.U. and reduced the power loss by 34.2%, from 202.67 to 133.17 KW.https://journal.uokufa.edu.iq/index.php/kje/article/view/16197binary dolphin echolocation optimization (bdeo)radial distribution networks (rdns)matlabpower lossvoltage profile |
| spellingShingle | Omar Muhammed Neda OPTIMAL DISTRIBUTION NETWORK RECONFIGURATION FOR LOSS MINIMIZATION AND VOLTAGE PROFILE IMPROVEMENT BASED ON ARTIFICIAL INTELLIGENCE Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ binary dolphin echolocation optimization (bdeo) radial distribution networks (rdns) matlab power loss voltage profile |
| title | OPTIMAL DISTRIBUTION NETWORK RECONFIGURATION FOR LOSS MINIMIZATION AND VOLTAGE PROFILE IMPROVEMENT BASED ON ARTIFICIAL INTELLIGENCE |
| title_full | OPTIMAL DISTRIBUTION NETWORK RECONFIGURATION FOR LOSS MINIMIZATION AND VOLTAGE PROFILE IMPROVEMENT BASED ON ARTIFICIAL INTELLIGENCE |
| title_fullStr | OPTIMAL DISTRIBUTION NETWORK RECONFIGURATION FOR LOSS MINIMIZATION AND VOLTAGE PROFILE IMPROVEMENT BASED ON ARTIFICIAL INTELLIGENCE |
| title_full_unstemmed | OPTIMAL DISTRIBUTION NETWORK RECONFIGURATION FOR LOSS MINIMIZATION AND VOLTAGE PROFILE IMPROVEMENT BASED ON ARTIFICIAL INTELLIGENCE |
| title_short | OPTIMAL DISTRIBUTION NETWORK RECONFIGURATION FOR LOSS MINIMIZATION AND VOLTAGE PROFILE IMPROVEMENT BASED ON ARTIFICIAL INTELLIGENCE |
| title_sort | optimal distribution network reconfiguration for loss minimization and voltage profile improvement based on artificial intelligence |
| topic | binary dolphin echolocation optimization (bdeo) radial distribution networks (rdns) matlab power loss voltage profile |
| url | https://journal.uokufa.edu.iq/index.php/kje/article/view/16197 |
| work_keys_str_mv | AT omarmuhammedneda optimaldistributionnetworkreconfigurationforlossminimizationandvoltageprofileimprovementbasedonartificialintelligence |