Fractional Order Darwinian PSO with Constraint Threshold for Load Flow Optimization of Energy Transmission System

Fractional Order Darwinian PSO with Constraint Threshold for Load Flow Optimization of Energy Transmission System

This paper present an effective optimization algorithm for Optimal Power Flow (OPF) inelectrical power systems. Fractional Order Darwinian Particle Swarm Optimization (FODPSO)algorithm is modified with constraint threshold limitation mechanism to acheive OPF. Resultsof the proposed method are compared on a part of 13 bus-bar 154 kV Eastern AnatoliaTransmission System and on a 14 bus-bar IEEE test system. In addition, the transmissionsystem is modeled by DigSilent software to analyse without taking any risk that may occur inreal systems. Thus, optimal parameter settings can be recommended for real time transmissionsystem.

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  • Ghanghro, S.P., Sahito, A., Memon, S., Jumani, M., Tunio, S., “Network Reconfiguration for Power Loss Reduction in Distribution System”, Sindh University Research Journal-SURJ, 48(1): 53-56, (2016).
  • Ela, E.L., Abou, A. A., Abido, M.A., Spea, S.R., “Optimal power flow using differential evolution algorithm”, Electric Power Systems Research, 80(7): 878-885, (2010).
  • Abaci, K., Yamacli, V., Akdağlı, A., “Optimal power flow with SVC devices by using the artificial bee colony algorithm”, Turkish Journal of Electrical Engineering & Computer Sciences, 24(1): 341-353, (2016).
  • Adaryani, M.R., Karami, A., “Artificial bee colony algorithm for solving multi-objective optimal power flow problem”, International Journal of Electrical Power& Energy Systems, 53, 219-230, (2013).
  • Bouchekara, H.R.E.H., “Optimal power flow using black-hole-based optimization approach”, Applied Soft Computing, 24, 879-888, (2014).
  • Zhang, X., Yu, T., Yang, B., Cheng, L., “Accelerating bio-inspired optimizer with transfer reinforcement learning for reactive power optimization”, Knowledge-Based Systems, 116, 26-38, (2017).
  • Nikham, T., Rasoul, N.M., Jabbari, M., Malekpour, A.R., “A modified shuffle frog leaping algorithm for multi-objective optimal power flow”, Energy, 36(11): 6420-6432, (2011).
  • Abido, M.A., “Optimal design of power-system stabilizers using particle swarm optimization”, IEEE Transactions on Energy Conversion, 17(3): 406-413, (2002).
  • Kadir, A.F.A, Mohamed, A., Shareef, H., Wanik, M.Z.C., “Optimal placement and sizing of distributed generations in distribution systems for minimizing losses and THD_v using evolutionary programming”, Turkish Journal of Electrical Engineering & Computer Sciences, 21(Sup.2): 2269-2282, (2013).
  • Lahmiri, S., Boukadoum, M., “An evaluation of particle swarm optimization techniques in segmentation of biomedical images”, In Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, Canada, 1313-1320, (2014).
  • Xie, W., Li, Y., “An automatic fractional coefficient setting method of FODPSO for hyperspectral image segmentation”, In Satellite Data Compression, Communications, and Processing XI, International Society for Optics and Photonics, 9501, (2015).
  • Ryalat, M.H., Emmens, D., Hulse, M., Bell, D., Al-Rahamneh, Z., Laycock, S., Fisher, M., “Evaluation of particle swarm optimization for medical image segmentation”, In International Conference on Systems Science, Hawaii, 61-72, (2016).
  • Internet: “The Institute of Electrical and Electronics Engineers 14 Bus System” https://hvdc.ca/uploads/knowledge_base/ieee_14_bus_technical_note.pdf?, (2018).
  • Kennedy, J., Eberhart, R.A., “New optimizer using particle swarm theory”, In Proceedings of IEEE Sixth International Symposium on Micro Machine Human Science, Nagoya, 39–43, (1995).
  • Valle, Y.D., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.C., Harley, R., “Particle swarm optimization: Basic concepts, variants and applications in power systems”, IEEE Transactions on Evolutionary Computation, 12(2): 171–195, (2008).
  • Tillett, J., Rao, T.M., Sahin, F., Rao, R., Brockport, S., “Darwinian particle swarm optimization”, Proceedings of the 2nd Indian International Conference on Artificial Intelligence, Pune, 1474– 1487, (2005).
  • Pires, E.J., Machado, J.A., Cunha, P.B., Mendes, L., “Particle swarm optimization with fractionalorder velocity”, Journal on Nonlinear Dynamics, 61(1–2): 295–301, (2010).
  • Couceiro, M.S., Ghamisi, P., “Fractional Order Darwinian PSO: Applications and Evaluation of an Evolutionary Algorithm”, Springer Publishing Company, London, 1-75, (2015).
  • Ostalczyk, P.W., “A note on the Grünwald-Letnikov fractional-order backward-difference”, Physica Scripta, T136, 1-5, (2009).
  • Omkar, S.N., Mudigere, D., Naik, G.N., “Vector evaluated particle swarm optimization for multiobjective design optimization of composite structures”, Computers & structures, 86(1-2): 1-14, (2008).
  • Ayan, K., Kılıç, U., “Optimal reaktif güç akışının kaotik yapay arı kolonisi ile çözümü”, 6th International Advanced Technologies Symposium, Elazığ, 20-24, (2011).
  • Nachimuthu, D.S., Basha, R.J, “Reactive Power Loss Optimization for an IEEE 14-Bus Power System Using Various Algorithms”, IU-Journal of Electrical & Electronics Engineering, 14(1): 1737-1744, (2014).