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A new interactive sine cosine algorithm for loading margin stability improvement under contingency

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Abstract

In this paper, a new method called sine cosine algorithm is adapted in coordination with an interactive process to improve the power system security considering loading margin stability and faults at specified important branches. In this study, the loading margin stability is optimized in coordination with total cost, total power loss, total voltage deviation and voltage stability index. In order to locate the best loading margin stability, an initial global database containing suboptimized control variables is generated based on two indices named global and local critical reactive margin security related to generating units. The optimized loading margin stability is improved in coordination with the availability of reactive power of different shunt FACTS devices installed at particular locations. The robustness of the proposed planning strategy is validated on a small test system, the IEEE 30-Bus and to a large test system, the IEEE 118-Bus. Optimized results found confirmed clearly the improvement of loading margin stability at critical situations such as faults at specified branches.

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References

  1. Carpentier J (1962) Contribution ‘a l“etude du Dispatching Economique (Contribution to the study of economic dispatch). Bull Soc Fr Elect 3:431–447

    Google Scholar 

  2. Dommel HW, Tinney TF (1968) Optimal power flow solutions. IEEE Trans Power Appar Syst 87(5):1866e76

    Google Scholar 

  3. Mota-Palomino R, Quintana VH (1986) Sparse reactive power scheduling by a penalty-function linear programming technique. IEEE Trans Power Syst 1(3):31–39

    Article  Google Scholar 

  4. Alsac O, Scott B (1974) Optimal load flow with steady state security. IEEE Trans Power Appar Syst PAS-93(3):745–751

  5. Burchett RC, Happ HH, Vierath DR (1984) Quadratically convergent optimal power flow. IEEE Trans Power Appar Syst 103(11):3267–3276

    Article  Google Scholar 

  6. Sun DI, Ashley B, Brewer B, Hughes A, Tinney WF (1984) Optimal power flow by Newton approach. IEEE Trans Power Appar Syst 103(10):2864–2875

    Article  Google Scholar 

  7. Yan X, Quintana VH (1999) Improving an interior point based OPF by dynamic adjustments of step sizes and tolerances. IEEE Trans Power Syst 14(2):709–717

    Article  Google Scholar 

  8. Frank S, Steponavice I, Rebennak S (2012) Optimal power flow: a bibliographic survey I, formulations and deterministic methods. Int J Energy Syst 3(3):221–258

    Google Scholar 

  9. Frank S, Steponavice I (2012) Optimal power flow: a bibliographic survey II. Non-deterministic and hybrid methods, Energy Syst. 3(3):259–289

    Google Scholar 

  10. Capitanescu F (2016) Critical review of recent advances and further developments needed in AC optimal power flow. Electr Power Syst Res 136:57–68

    Article  Google Scholar 

  11. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  12. Mahdad B, Srairi K (2015) Blackout risk prevention in a smart grid based flexible optimal strategy using Grey Wolf-pattern search algorithms. Energy Convers Manag 98:411–429

    Article  Google Scholar 

  13. Sulaiman MH, Mustaffa Z, Mohamed MR, Aliman O (2015) Using the gray wolf optimizer for solving optimal reactive power dispatch problem. Appl Soft Comput 32:286–292

    Article  Google Scholar 

  14. Jangir P, Parmar SA, Trivedi IN, Bhesdadiya RH (2017) A novel hybrid Particle Swarm Optimizer with multi verse optimizer for global numerical optimization and optimal reactive power dispatch problem. Eng Sci Technol Int J 20(2):570–586

  15. Panda A, Tripathy M (2015) Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm. Energy 93:816–827

    Article  Google Scholar 

  16. Pandiarajan K, Babulal CK (2016) Fuzzy harmony search algorithm based optimal power flow for power system security enhancement. Int J Electr Power Energy Syst 78:72–79

    Article  Google Scholar 

  17. Mahdad B, Srairi k (2016) Security constrained optimal power flow solution using new adaptive partitioning flower pollination algorithm. Appl Soft Comput 46:501–522

    Article  Google Scholar 

  18. Othman MM, El-Khattam W, Hegazy YG, Abdelaziz AY (2016) Optimal placement and sizing of voltage controlled distributed generators in unbalanced distribution networks using supervised firefly algorithm. Electr Power Energy Syst 82:105–113

    Article  Google Scholar 

  19. Duman S (2016) Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones. Neural Comput Appl 1–15. doi:10.1007/s00521-016-2265-0

  20. Abaci K, Yamacli V (2016) Differential search algorithm for solving multi-objective optimal power flow problem. Electr Power Energy Syst 79:1–10

    Article  Google Scholar 

  21. Mohamed A-AA, Mohamed YS, El-Gaafary AAM, Hemeid AM (2017) Optimal power flow using moth swarm algorithm. Electr Power Syst Res 142:190–206

    Article  Google Scholar 

  22. Singh RP, Mukherjee V, Ghoshal SP (2016) Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem. Appl Soft Comput 40:161–177

    Article  Google Scholar 

  23. Xu Y, Yang H, Zhang R, Dong ZY, Lai M, Wong KP (2016) A contingency partitioning approach for preventive–corrective security-constrained optimal power flow computation. Electr Power Syst Res 132:132–140

    Article  Google Scholar 

  24. Kumar AR, Premalath L (2015) Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization. Electr Power Energy Syst 73:393–399

    Article  Google Scholar 

  25. Radosavljevic’ J, Jevtic’ M, Milovanovic’ M (2016) A solution to the ORPD problem and critical analysis of the results. Electr Eng. doi:10.1007/s00202-016-0503-1

    Google Scholar 

  26. Roberge V, Tarbouchi M, Okou F (2016) Optimal power flow based on parallel metaheuristics for graphics processing units. Electr Power Syst Res 140:344–353

  27. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133

  28. Kessel P, Glavitsch H (1986) Estimating the voltage stability of a power system. IEEE Trans Power Deliv 1:346–354. doi:10.1109/TPWRD.1986.4308013

    Article  Google Scholar 

  29. Zimmerman RD, Murillo-Sánchez CE, Thomas RJ (2011) MATPOWER: steady-state operations, planning and analysis tools for power systems research and education. IEEE Trans Power Syst 26(1):12–19

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Correspondence to Belkacem Mahdad.

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Mahdad, B., Srairi, K. A new interactive sine cosine algorithm for loading margin stability improvement under contingency. Electr Eng 100, 913–933 (2018). https://doi.org/10.1007/s00202-017-0539-x

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