OPTIMAL DISTRIBUTION NETWORK RECONFIGURATION FOR LOSS MINIMIZATION AND VOLTAGE PROFILE IMPROVEMENT BASED ON ARTIFICIAL INTELLIGENCE

Omar  Muhammed Neda

doi:10.30572/2018/KJE/160216

Authors

Omar Muhammed Neda Department of Electrical Engineering, Sunni Diwan Endowment, Iraq https://orcid.org/0000-0001-9748-3341

DOI:

https://doi.org/10.30572/2018/KJE/160216

Keywords:

Binary Dolphin Echolocation Optimization (BDEO), Radial Distribution Networks (RDNs), MATLAB, Power loss, Voltage profile

Abstract

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.

Downloads

Download data is not yet available.

References

Abed, R. H. 2024. Investigation of Hybrid Photovoltaic/Thermal Solar System Performance Under Iraq's Climate Conditions. Kufa Journal of Engineering, 15, 1-18. DOI: https://doi.org/10.30572/2018/kje/150101

Al-Jabari, A., Korkmaz, F. & Teke, M. 2022. A Simulation of Solar Energy System Controlled By P&O, Ic And Fuzzy Logic Using Bidirectional Charging Of Battery. Kufa Journal Of Engineering, 13, 41-58. DOI: https://doi.org/10.30572/2018/kje/130303

Al-Mamoori, D. H., Aljanabi, M. H., Neda, O. M., Al-Tameemi, Z. H. & Alobaidi, A. A. 2019. Evaluation of gas fuel and biofuel usage in turbine. Indonesian Journal of Electrical Engineering and Computer Science, 14, 1097-1104. DOI: https://doi.org/10.11591/ijeecs.v14.i3.pp1097-1104

Al-Tameemi, Z. H., Neda, O. M., Jumaa, F. A., Al-Mamoori, D. H., Aljanabi, M. H. & Jasim, J. M. Optimal sizing and location of DG units for enhancing voltage profile and minimizing real power losses in the radial power systems based on PSO technique. IOP Conference Series: Materials Science and Engineering, 2019. IOP Publishing, 042021. DOI: https://doi.org/10.1088/1757-899X/518/4/042021

Aman, M., Jasmon, G., Bakar, A. & Mokhlis, H. 2014. Optimum network reconfiguration based on maximization of system loadability using continuation power flow theorem. International journal of electrical power & energy systems, 54, 123-133. DOI: https://doi.org/10.1016/j.ijepes.2013.06.026

Andervazh, M. R., Olamaei, J. & Haghifam, M. R. 2013. Adaptive multi‐objective distribution network reconfiguration using multi‐objective discrete particles swarm optimisation algorithm and graph theory. IET Generation, Transmission & Distribution, 7, 1367-1382. DOI: https://doi.org/10.1049/iet-gtd.2012.0712

Bai, W., Zhang, W., Allmendinger, R., Enyekwe, I. & Lee, K. Y. 2024. A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary Algorithms. Energies, 17, 511. DOI: https://doi.org/10.3390/en17020511

Chidanandappa, R., Ananthapadmanabha, T. & Ranjith, H. 2015. Genetic algorithm based network reconfiguration in distribution systems with multiple DGs for time varying loads. Procedia technology, 21, 460-467. DOI: https://doi.org/10.1016/j.protcy.2015.10.023

Daryan, A. S., Palizi, S. & Farhoudi, N. 2019. Optimization of plastic analysis of moment frames using modified dolphin echolocation algorithm. Advances in Structural Engineering, 22, 2504-2516. DOI: https://doi.org/10.1177/1369433219845151

De Oliveira, L. W., De Oliveira, E. J., Gomes, F. V., Silva Jr, I. C., Marcato, A. L. & Resende, P. V. 2014. Artificial immune systems applied to the reconfiguration of electrical power distribution networks for energy loss minimization. International Journal of Electrical Power & Energy Systems, 56, 64-74. DOI: https://doi.org/10.1016/j.ijepes.2013.11.008

Fadhil, G., Abed, I. & Jasim, R. 2021. Genetic algorithm utilization to fine tune the parameters of PID controller. Kufa Journal of Engineering, 12, 1-12. DOI: https://doi.org/10.30572/2018/kje/120201

Gautam, R., Khadka, S., Malla, T. B., Bhattarai, A., Shrestha, A. & Gonzalez-Longatt, F. 2024. Assessing uncertainty in the optimal placement of distributed generators in radial distribution feeders. Electric Power Systems Research, 230, 110249. DOI: https://doi.org/10.1016/j.epsr.2024.110249

Imran, A. M. & Kowsalya, M. 2014. A new power system reconfiguration scheme for power loss minimization and voltage profile enhancement using fireworks algorithm. International Journal of Electrical Power & Energy Systems, 62, 312-322. DOI: https://doi.org/10.1016/j.ijepes.2014.04.034

Imran, A. M., Kowsalya, M. & Kothari, D. 2014. A novel integration technique for optimal network reconfiguration and distributed generation placement in power distribution networks. International Journal of Electrical Power & Energy Systems, 63, 461-472. DOI: https://doi.org/10.1016/j.ijepes.2014.06.011

Jumaa, F. A., Neda, O. M. & Mhawesh, M. A. 2021. Optimal distributed generation placement using artificial intelligence for improving active radial distribution system. Bulletin of Electrical Engineering and Informatics, 10, 2345-2354. DOI: https://doi.org/10.11591/eei.v10i5.2949

Kaveh, A. & Farhoudi, N. 2013. A new optimization method: Dolphin echolocation. Advances in Engineering Software, 59, 53-70. DOI: https://doi.org/10.1016/j.advengsoft.2013.03.004

Kumar, P. H. & Rudramoorthy, M. 2021. Distribution network reconfiguration considering DGs using a hybrid CS-GWO algorithm for power loss minimization and voltage profile enhancement. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 9, 880-906. DOI: https://doi.org/10.52549/ijeei.v9i4.3432

Mam, M., Leena, G. & Saxena, N. 2016. Distribution network reconfiguration for power loss minimization using bacterial foraging optimization algorithm. International Journal of Engineering and Manufacturing (IJEM), 6, 18-32. DOI: https://doi.org/10.5815/ijem.2016.02.03

Mhawesh, M. A., Al-Tameemi, Z. H. & Neda, O. M. 2020. Review of mobile robots’ obstacle avoidance, localization, motion planning, and wheels. Indonesian Journal of Electrical Engineering and Computer Science, 20, 768-776. DOI: https://doi.org/10.11591/ijeecs.v20.i2.pp768-776

Neda, O. M. & Ma’arif, A. 2022. Chaotic Particle Swarm Optimization for Solving Reactive Power Optimization Problem. International Journal of Robotics and Control Systems, 1, 523-533. DOI: https://doi.org/10.31763/ijrcs.v1i4.539

Neda, O. M. 2020a. A new hybrid algorithm for solving distribution network reconfiguration under different load conditions. Indonesian Journal of Electrical Engineering and Computer Science, 20, 1118-1127. DOI: https://doi.org/10.11591/ijeecs.v20.i3.pp1118-1127

Neda, O. M. 2020b. Optimal coordinated design of PSS and UPFC-POD using DEO algorithm to enhance damping performance. International Journal of Electrical and Computer Engineering (IJECE), 10, 6111-6121. DOI: https://doi.org/10.11591/ijece.v10i6.pp6111-6121

Neda, O. M. 2022. A Novel Technique for Optimal siting and Rating of Shunt Capacitors Placed to the Radial Distribution Systems. Advances in Electrical and Electronic Engineering, 20, 143-153. DOI: https://doi.org/10.15598/aeee.v20i2.4415

Neda, O. M. 2024. Hybrid design of optimal reconfiguration and DG sizing and siting using a novel improved salp swarm algorithm. Electrical Engineering, 1-16. DOI: https://doi.org/10.1007/s00202-024-02493-7

Neda, O. M. A Novel Technique For Optimal Allocation Of Rdg Units On Distribution Network. 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2021. IEEE, 1-5. DOI: https://doi.org/10.1109/ICECCT52121.2021.9616633

Ng, H., Salama, M. & Chikhani, A. 2000. Classification of capacitor allocation techniques. IEEE Transactions on power delivery, 15, 387-392. DOI: https://doi.org/10.1109/61.847278

Nguyen, T. T., Nguyen, N. A., Duong, T. L., Ngo, T. Q. & Bach, T. 2022. Modified sunflower optimization for network reconfiguration and distributed generation placement. International Journal of Electrical and Computer Engineering (IJECE), 12, 5765-5774. DOI: https://doi.org/10.11591/ijece.v12i6.pp5765-5774

Otuo-Acheampong, D., Rashed, G. I., Akwasi, A. M. & Haider, H. 2023. Application of optimal network reconfiguration for loss minimization and voltage profile enhancement of distribution system using heap-based optimizer. International Transactions on Electrical Energy Systems, 2023. DOI: https://doi.org/10.1155/2023/9930954

Rao, R. S., Ravindra, K., Satish, K. & Narasimham, S. 2012. Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation. IEEE transactions on power systems, 28, 317-325. DOI: https://doi.org/10.1109/TPWRS.2012.2197227

Raut, U. & Mishra, S. 2020. An improved sine–cosine algorithm for simultaneous network reconfiguration and DG allocation in power distribution systems. Applied Soft Computing, 92, 106293. DOI: https://doi.org/10.1016/j.asoc.2020.106293

Saedi Daryan, A., Salari, M., Farhoudi, N. & Palizi, S. 2021. Seismic design optimization of steel frames with steel shear wall system using modified Dolphin algorithm. International Journal of Steel Structures, 21, 771-786. DOI: https://doi.org/10.1007/s13296-021-00472-3

Salkuti, S. R. & Battu, N. R. 2021. An effective network reconfiguration approach of radial distribution system for loss minimization and voltage profile improvement. Bulletin of Electrical Engineering and Informatics, 10, 1819-1827. DOI: https://doi.org/10.11591/eei.v10i4.2867

Sambaiah, K. S. & Jayabarathi, T. 2021. Optimal reconfiguration and renewable distributed generation allocation in electric distribution systems. International Journal of Ambient Energy, 42, 1018-1031. DOI: https://doi.org/10.1080/01430750.2019.1583604

Sarfi, R. J., Salama, M. & Chikhani, A. 1994. A survey of the state of the art in distribution system reconfiguration for system loss reduction. Electric Power Systems Research, 31, 61-70. DOI: https://doi.org/10.1016/0378-7796(94)90029-9

Su, C.-T., Chang, C.-F. & Chiou, J.-P. 2005. Distribution network reconfiguration for loss reduction by ant colony search algorithm. Electric Power Systems Research, 75, 190-199. DOI: https://doi.org/10.1016/j.epsr.2005.03.002

Verma, H. K. & Singh, P. 2018. Optimal reconfiguration of distribution network using modified culture algorithm. Journal of The Institution of Engineers (India): Series B, 99, 613-622. DOI: https://doi.org/10.1007/s40031-018-0344-6