Transactions on Machine Intelligence

Transactions on Machine Intelligence

Optimal Placement of Protective Relays and Distributed Generation Units in Distribution Networks for Enhancing System Reliability Using the PSO Algorithm

Document Type : Original Article

Author
M. Khoddam
Assistant Professor, Department of Electrical Engineering, Khomein Branch, Islamic Azad University, Khomein, Iran
10.47176/TMI.2019.205
Abstract
The integration of Distributed Generation (DG) into modern distribution networks introduces several operational challenges, particularly in terms of network protection and reliability. One of the key challenges for distribution network operators is the transition from a traditional radial structure to a more complex meshed configuration due to the presence of DG sources. This structural change alters the short-circuit levels, rendering conventional fixed protection settings ineffective. As a result, the placement and coordination of protective devices such as overcurrent relays, reclosers, and fuses must be carefully reconsidered to ensure effective fault detection and system reliability. This paper proposes an optimization-based algorithm for determining the optimal locations for installing protective devices and DG units within a distribution network. The proposed methodology aims to enhance system reliability by strategically placing protection equipment while considering the impact of DG integration. The objective function in this study is to minimize key reliability indices, such as the System Average Interruption Duration Index (SAIDI) and the System Average Interruption Frequency Index (SAIFI), while also reducing network losses. The effectiveness of the proposed approach is evaluated using simulation studies conducted on standard test distribution systems. The results demonstrate that the optimized placement of DG and protection devices significantly improves network resilience, enhances fault detection capabilities, and reduces overall power losses. This study provides a comprehensive framework for improving the reliability and efficiency of distribution networks in the presence of DG, contributing to the development of smarter and more adaptive power systems.
Keywords
Optimal Placement
Reliability
Distribution Network Protection
Distributed Generation Sources
PSO Algorithm
  • Nunna, H. K., & Srinivasan, D. (2017). Multiagent-based transactive energy framework for distribution systems with smart microgrids. IEEE Transactions on Industrial Informatics, 13(5), 2241–2250. https://doi.org/10.1109/TII.2017.2679808
  • Saint-Pierre, A., & Mancarella, P. (2017). Active distribution system management: A dual-horizon scheduling framework for DSO/TSO interface under uncertainty. IEEE Transactions on Smart Grid, 8(5), 2186–2197. https://doi.org/10.1109/TSG.2016.2518084
  • Fallah, S. N., Deo, R., Shojafar, M., Conti, M., & Shamshirband, S. (2018). Computational intelligence approaches for energy load forecasting in smart energy management grids: State of the art, future challenges, and research directions. Energies, 11(3), 596. https://doi.org/10.3390/en11030596
  • Jin, Q., & Kirk, M. (2018). pH as a primary control in environmental microbiology: 1. Thermodynamic perspective. Frontiers in Environmental Science, 6, 21. https://doi.org/10.3389/fenvs.2018.00021
  • Wei, Y., Yu, F., Song, M., & Han, Z. (2018). User scheduling and resource allocation in HetNets with hybrid energy supply: An actor-critic reinforcement learning approach. IEEE Transactions on Wireless Communications, 17(2), 680–692. https://doi.org/10.1109/TWC.2017.2769644
  • Donalvo, M. T. G., Tio, A. E. D. C., & Tarnete, W. R. D. (2015). Maximizing reliability by optimal sitting of distributed generation and protective devices. In IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC) (pp. 1–6). IEEE. https://doi.org/10.1109/EEEIC.2015.7165205
  • Dewedasa, M., Ghosh, A., & Ledwich, G. (2011). M4: Microgrid operation and control executive summary. CSIRO Intelligent Grid Research Cluster-Project 7, Tech. Rep.
  • Soudi, F., & Tomsovic, K. (1999). Optimal distribution protection design: Quality of solution and computational analysis. Electric Power Systems Research, 21(4), 327–335. https://doi.org/10.1016/S0142-0615(98)00052-0
  • IEEE Working Group on System Design. (2000). Trial use guide for electric power distribution reliability indices (Report No. 1366, Draft No. 14).
  • Billinton, R., & Jonnavithula, S. (1996). Optimal switching device placement in radial distribution systems. IEEE Transactions on Power Systems, 11(3), 1646–1651. https://doi.org/10.1109/61.517529
  • Celli, G., & Pilo, F. (1999). Optimal sectionalizing switches allocation in distribution networks. IEEE Transactions on Power Delivery, 14(3), 1167–1172. https://doi.org/10.1109/61.772388
  • Soudi, F., & Tomasovic, K. (2001). Optimal trade-off in distribution protection design. IEEE Transactions on Power Delivery, 16(2), 292–296. https://doi.org/10.1109/61.915498
  • Silva, L. G. W., Pereira, R. A. F., & Mantovani, J. R. S. (2005). Optimized allocation of sectionalizing switches and protection devices in distribution networks by using reactive tabu search algorithm. In Proceedings of the 18th International Conference on Electricity Distribution (pp. 1–5). https://doi.org/10.1049/cp:20051101
  • Silva, L. G. W., Pereira, R. A. F., Abbad, J. R., & Mantovani, J. R. S. (2008). Optimized placement of control and protective devices in electric distribution systems through reactive tabu search algorithm. Electric Power Systems Research, 78(2), 372–381. https://doi.org/10.1016/j.epsr.2007.03.005
  • Silva, L. G. W., Pereira, R. A. F., & Mantovani, J. R. S. (2004). Optimized allocation of sectionalizing switches and control and protection devices for reliability indices improvement in distribution systems. In Proceedings of the IEEE/PES Transmission & Distribution Conference & Exposition: Latin America (pp. 1–6). IEEE.
  • Hashemi Dezaki, H., Askarian Abyaneh, H., Yaghubinia, M. R., Zanganeh, M., & Mazlumi, K. (2011). Protective and switching devices allocation according to total cost minimization by genetic algorithm in distribution systems. In Proceedings of the 7th International Conference on Electrical and Electronics Engineering (ELECO) (pp. 1–6). IEEE.
  • Nascimento Alves, H. (2012). A hybrid algorithm for optimal placement of switches device in electric distribution systems. IEEE Latin America Transactions, 10(6), 2218–2223. https://doi.org/10.1109/TLA.2012.6418125
  • Jafari, M., Monsef, H., & Moghadam, S. Z. (2011). Optimal number, placement and capacity of DGs and reclosers using analytic hierarchical process and genetic algorithm. In Proceedings of the 10th International Conference on Environment and Electrical Engineering (EEEIC) (pp. 1–6). IEEE. https://doi.org/10.1109/EEEIC.2011.5874832
  • Mohamadi, M., & Fotohi, M. (2019). Optimal placement of switching and protection device in radial distribution networks to enhance system reliability using the AHP-PSO method. Turkish Journal of Electrical Engineering and Computer Science, 27(1), 181–196. https://doi.org/10.3906/elk-1806-130
  • Mohammed, K. K., & Ebadi, J. (2017). Optimal placement of sectionalizing switches in real distribution network considering feeder ranking. Journal of Knowledge-based Engineering and Innovation, 3(10), 734–742.
  • Haddadnia, J., Seryasat, O. R., & Rabiee, H. (2014). Fault detection of induction motor ball bearings. Advances in Environmental Biology, 1802-1810.
  • Mungkung, N., Gomurut, N., & Tanitteerapan, T. (2009). Analysis of technical loss in distribution line system. In Proceedings of the 8th International Conference on Telecommunications and Informatics (pp. 1–6).
  • Eberhart, R. C., & Shi, Y. (2001). Particle swarm optimization: Developments, applications, and resources. In Proceedings of the 2001 Congress on Evolutionary Computation (Vol. 1, pp. 81–86). IEEE. https://doi.org/10.1109/CEC.2001.934374
Transactions on Machine Intelligence
Volume 2, Issue 4
Autumn 2019
Pages 205-212

  • Receive Date 10 July 2019
  • Revise Date 14 October 2019
  • Accept Date 05 December 2019