FCC Algorithm for Power Loss Diminution | Journal of Engineering Sciences

FCC Algorithm for Power Loss Diminution

Author(s): Kanagasabai L.

Affiliation(s): Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, 520007 Andhra Pradesh, India.

*Corresponding Author’s Address: [email protected]

Issue: Volume 8, Issue 1 (2021)

Dates:
Received: February 19, 2021
The final version received: May 29, 2021
Accepted for publication: June 7, 2021

Citation:
Kanagasabai, L. (2021). FCC algorithm for power loss diminution. Journal of Engineering Sciences, Vol. 8(1), pp. E29–E38, doi: 10.21272/jes.2021.8(1).e5

DOI: 10.21272/jes.2021.8(1).e5

Research Area:  MECHANICAL ENGINEERING: Computational Mechanics

Abstract. In this work, the FCC algorithm has been applied to the power problem. Real power loss reduction, voltage deviation minimization, and voltage stability enhancement are the key objectives of the proposed work. The proposed FCC algorithm has been modeled based on the competition, communication among teams, and training procedure within the team. The solution has been created based on the team, players, coach, and substitution tactic. A preliminary solution of the problem is produced, and the initialization of the teams depends on the team’s formation with substitute tactics. Mainly fitness function for each solution is computed, and it plays an imperative role in the process of the algorithm. With the performance in the season, promotion and demotion of the teams will be there. Most excellently performed teams will be promoted to a senior division championship, and the most poorly performed team will be demoted to the top lower division league. Ideas and tactics sharing procedure, repositioning procedure, Substitution procedure, seasonal transmit procedure, Promotion and demotion procedure of a team which plays in the confederation cup has been imitated to solve the problem. Similar to an artificial neural network, a learning phase is also applied in the projected algorithm to improve the quality of the solution. Modernization procedure employed sequentially to identify the best solution. With and without voltage stability (L-index) FCC algorithm is evaluated in IEEE 30, bus system. Then the Proposed FCC algorithm has been evaluated in standard IEEE 14, 57,118,300 bus test systems without L-index. Power loss minimization and voltage stability index improvement have been achieved with voltage deviation minimization.

Keywords: optimal reactive power, transmission loss, FCC algorithm.

References:

  1. Lee, K. Y. (1984). Fuel-cost minimisation for both real and reactive-power dispatches. Proceedings Generation, Transmission and Distribution Conference, Vol. 131(3), pp. 85–93.
  2. Deeb, N. I. (1998). An efficient technique for reactive power dispatch using a revised linear programming approach. Electric Power System Research, Vol. 15(2), pp. 121–134.
  3. Bjelogrlic, M. R., Calovic, M. S., Babic, B. S. (1990). Application of Newton’s optimal power flow in voltage/reactive power control. IEEE Trans Power System, Vol. 5(4), pp. 1447–1454.
  4. Granville, S. (1994). Optimal reactive dispatch through interior point methods. IEEE Transactions on Power System, Vol. 9(1), pp. 136–146, http://dx.doi.org/10.1109/59.317548.
  5. Grudinin, N. (1998). Reactive power optimization using successive quadratic programming method. IEEE Transactions on Power System, Vol. 13(4), pp. 1219–1225, doi: 10.1109/59.736232.
  6. Roy, P. K., Dutta, S. (2019). Economic load dispatch: Optimal power flow and optimal reactive power dispatch concept. Optimal Power Flow Using Evolutionary Algorithms, IGI Global, Vol. 2019, pp. 46–64, doi: 10.4018/978-1-5225-6971-8.ch002.
  7. Bingane, C., Anjos, M. F., Le Digabel, S. (2019) Tight-and-cheap conic relaxation for the optimal reactive power dispatch problem. IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2019.2912889.
  8. Prasad D., Mukherjee, V. (2018). Solution of optimal reactive power dispatch by symbiotic organism search algorithm incorporating FACTS devices. IETE Journal of Research, Vol. 64(1), pp. 149–160, doi: 10.1080/03772063.2017.1334600.
  9. Aljohani, T. M., Ebrahim, A. F., Single, O. M. (2019). Multiobjective optimal reactive power dispatch based on hybrid artificial physics – Particle swarm optimization. Energies, Vol. 12(12), 2333, doi: 10.3390/en12122333.
  10. Mahate, R. K., Singh, H. (2019). Multi-objective optimal reactive power dispatch using differential evolution. International Journal of Engineering Technologies and Management Research, Vol. 6(2), pp. 27–38, doi: 10.5281/zenodo.2585477.
  11. Yalçın, E., Taplamacıoğlu, M., Çam, E. (2019). The adaptive chaotic symbiotic organisms search algorithm proposal for optimal reactive power dispatch problem in power systems, Electrica, Vol. 19, pp. 37-47.
  12. Mouassa, S., Bouktir, T. (2019). Multi-objective ant lion optimization algorithm to solve large-scale multi-objective optimal reactive power dispatch problem. COMPEL – The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol. 38(1), pp. 304–324, doi: 10.1108/COMPEL-05-2018-0208.
  13. Basu, M. (2016). Quasi-oppositional differential evolution for optimal reactive power dispatch. Electrical Power and Energy Systems, Vol. 78, pp. 29–40.
  14. Teeparthi, K., Kumar, D. V. (2017). Multi-objective hybrid PSO-APO algorithm-based security constrained optimal power flow with wind and thermal generators. Eng. Sci. Technol. Int. J., Vol. 20, pp. 411–426.
  15. Teeparthi, K., Kumar, D. V. (2017). Dynamic power system security analysis using a hybrid PSO-APO algorithm. Eng. Technol. Appl. Sci. Res., Vol. 7, pp. 2124–2131.
  16. Ramírez, M., Castellanos, R., Calderón, G., Malik, O. (2018). Placement and sizing of battery energy storage for primary frequency control in an isolated section of the Mexican power system. Electric Power Systems Research, Vol. 160, pp. 142–150.
  17.  Rodríguez-Gallegos, C. D., Yang, D., Gandhi, O., Bieri, M., Reindl, T., Panda, S. K. (2018). A multi-objective and robust optimization approach for sizing and placement of PV and batteries in off-grid systems fully operated by diesel generators. An Indonesian case study. Energy, Vol. 160, pp. 410–429.
  18. Beigvand, S. D., Abdi, H., La Scala, M. (2016). Combined heat and power economic dispatch problem using gravitational search algorithm. Electr. Power Syst. Res., Vol. 133, pp. 160–172.
  19. Narang, N., Sharma, E., Dhillon, J. S. (2017). Combined heat and power economic dispatch using integrated civilized swarm optimization and Powell’s pattern search method. Appl. Soft Comput., Vol. 52, pp. 190–202.
  20. Warid, W., Hizam, H., Mariun, N., Wahab, N. I. A. (2018). A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution. Applied Soft Computing Journal, Vol. 65, pp. 360–373.
  21. Herbadji, O., Slimani, L., Bouktir, T. (2017). Multiobjective optimal power flow considering the fuel cost, emission, voltage deviation and power losses using multi-objective dragonfly algorithm. International Conference on Recent Advances in Electrical Systems, pp. 191–197.
  22. Vaisakh, K., Rao, P. K. (2008). Optimum Reactive Power Dispatch Using Differential Evolution for Improvement of Voltage Stability, IEEE.
  23. Chavan, S. D., Adgokar, N. P. (2015). An overview on particle swarm optimization: Basic concepts and modified variants. International Journal of Science and Research, Vol. 4(5), pp. 255–260.
  24. Nagendra, P., Dey, S. H. N., Paul, S. (2014). Voltage stability assessment of a power system incorporating FACTS controllers using unique network equivalent. Ain Shams Eng. J., Vol. 5(1), pp. 103–111.
  25. Nagendra, P., Dey, S. H. N., Paul, S. (2015). Location of static VAR compensator in a multi-bus power system using unique network equivalent. Adv. Energy Res., Vol. 3(4), pp. 235–249.
  26. Zhang, H., Lei, X., Wang, C., Yue, D., Xie, X. (2017). Adaptive grid based multi-objective Cauchy differential evolution for stochastic dynamic economic emission dispatch with wind power uncertainty. PLOS ONE, Vol. 12(9), e0185454, doi: 10.1371/journal.pone.0185454.
  27. Bindu, K. N., Kumar, K. K. (2016). Combined economic and emission dispatch using random drift particle swarm optimization. International Journal for Modern Trends in Science and Technology, Vol. 2(11), pp. 134–139.
  28. Rupa, J. M., Ganesh, S. (2014). Power flow analysis for radial distribution system using backward/forward sweep method. Inter J Electr, Comput, Electron Commun Eng, Vol. 8, pp. 1540–1544.
  29. Abdel-Akher, M. (2013). Voltage stability analysis of unbalanced distribution systems using backward/forward sweep load-flow analysis method with secant predictor. IET Gener, Transm Distrib, Vol. 7, pp. 309–317.
  30. Prasad, C. D., Kumar, G. P. (2016). Effect of load parameters variations on AGC of single area thermal power system in presence of integral and PSO-PID controllers. 2015 Conf. Power, Control. Common. Compute. Technol. Sustain. Growth, PCCCTSG 2015, Vol. 1, pp. 64–68.
  31. Morsali, J., Zare, K., Hagh, M. T. (2016). Performance comparison of TCSC with TCPS and SSSC controllers in AGC of realistic interconnected multi – sources power system. Ain Shams Engineering Journal, Vol. 7(1), pp. 143–158, doi: 10.1016/j.asej.2015.11.012.
  32. Arifoğlu, U., Yalçin, F. (2018). System constrained active power loss minimization in practical multi-terminal HVDC systems through GA. Sakarya University Journal of Science, Vol. 22(4), pp. 1163–1173, doi: 10.16984/saufenbilder.421351.
  33. Wei, H., Lin, C. and Wang, Y. (2018). The optimal reactive power flow model in mixed polar form based on transformer dummy nodes. IEEJ Trans Elec Electron Eng, Vol. 13, pp. 411–416.
  34. Fang, S, Cheng, H, Xu, G, Zhou, Q, He, H, Zeng, P. (2017). Stochastic optimal reactive power reserve dispatch considering voltage control areas. Int. Trans. Electr. Energ. Syst., Vol. 27, e2269.
  35. Dozein, M. G., Monsef, H., Ansari, J., Kazemi, A. (2016). An effective decentralized scheme to monitor and control the reactive power flow: a holonic‐based strategy. Int. Trans. Electr. Energ. Syst., Vol. 26, pp. 1184–1209.
  36. Du, Z., Nie, Y. and Liao, P. (2014). PCPDIPM‐based optimal reactive power flow model using augmented rectangular coordinates. Int. Trans. Electr. Energ. Syst., Vol. 24, pp. 597–608.
  37. Liu, B., Liu, F., Zhai, B. and Lan, H. (2019). Investigating continuous power flow solutions of IEEE 14‐bus system. IEEJ Trans Elec Electron Eng, Vol. 14, pp. 157–159.
  38. Soodi, H. A., Vural, A. M. (2018). STATCOM estimation using back-propagation, PSO, shuffled frog leap algorithm, and genetic algorithm based neural networks. Comput Intell Neurosci., Vol. 2018, 6381610.
  39. Dai, C., Chen, W., Zhu, Y., Zhang, X. (2009). Seeker optimization algorithm for optimal reactive power dispatch. IEEE T. Power Syst., Vol. 24(3), pp. 1218–1231.
  40. El Ela, A. A., Abido, M. A., Spea, S. R. (2011). Differential evolution algorithm for optimal reactive power dispatch. Electr. Power Syst. Res., Vol. 81, pp. 458–464.
  41. Duman, S., Sönmez, Y., Güvenç, U., Yörükeren, N. (2012). Optimal reactive power dispatch using a gravitational search algorithm. IET Gener. Transm. Distrib., Vol. 6, pp. 563–576.
  42. Aljohani, T. M., Ebrahim, A. F., Mohammed, O. (2019). Single and multiobjective optimal reactive power dispatch based on hybrid artificial physics–particle swarm optimization. Energies, Vol. 12, 2333.
  43. Subbaraj, P., Rajnarayan, P. N. (2009). Optimal reactive power dispatch using self-adaptive real coded genetic algorithm. Electr. Power Syst. Res., Vol. 79(2), pp. 374–381.
  44. Pandya, S., Roy, R. (2015). Particle swarm optimization based optimal reactive power dispatch. Proceeding of the IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1–5, doi: 10.1109/ICECCT.2015.7225981.
  45. Hussain, A. N., Abdullah, A. A., Neda, O. M. (2018). Modified particle swarm optimization for solution of reactive power dispatch. Research Journal of Applied Sciences, Engineering and Technology, Vol. 15(8), pp. 316–327, doi: 10.19026/rjaset.15.5917.
  46. Reddy, S. S. (2017). Optimal reactive power scheduling using cuckoo search algorithm. International Journal of Electrical and Computer Engineering, Vol. 7(5), pp. 2349–2356.
  47. Szepesi, D., van’t Erve, A.H. (1984). Adaptive clamping control on high performance CNC lathes. In: Davies B.J. (eds) Proceedings of the Twenty-Fourth International Machine Tool Design and Research Conference. Palgrave, London, pp. 177–186, doi: 10.1007/978-1-349-81247-9_25.

Full Text




© 2014-2024 Sumy State University
"Journal of Engineering Sciences"
ISSN 2312-2498 (Print), ISSN 2414-9381 (Online).
All rights are reserved by SumDU