Heat Transfer and Simulated Coronary Circulation System Optimization Algorithms for Real Power Loss Reduction | Journal of Engineering Sciences

Heat Transfer and Simulated Coronary Circulation System Optimization Algorithms for Real Power Loss Reduction

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: January 27, 2021
The final version received: April 19, 2021
Accepted for publication: April 25, 2021

Citation:
Kanagasabai L. (2021). Heat transfer and simulated coronary circulation system optimization algorithms for real power loss reduction. Journal of Engineering Sciences, Vol. 8(1), pp. E1–E8, doi: 10.21272/jes.2021.8(1).e1

DOI: 10.21272/jes.2021.8(1).e1

Research Area:  MECHANICAL ENGINEERING: Computational Mechanics

Abstract. In this paper, the heat transfer optimization (HTO) algorithm and simulated coronary circulation system (SCCS) optimization algorithm has been designed for Real power loss reduction. In the projected HTO algorithm, every agent is measured as a cooling entity and surrounded by another agent, like where heat transfer will occur. Newton’s law of cooling temperature will be updated in the proposed HTO algorithm. Each value of the object is computed through the objective function. Then the objects are arranged in increasing order concerning the objective function value. This projected algorithm time “t” is linked with iteration number, and the value of “t” for every agent is computed. Then SCCS optimization algorithm is projected to solve the optimal reactive power dispatch problem. Actions of human heart veins or coronary artery development have been imitated to design the algorithm. In the projected algorithm candidate solution is made by considering the capillaries. Then the coronary development factor (CDF) will appraise the solution, and population space has been initiated arbitrarily. Then in the whole population, the most excellent solution will be taken as stem, and it will be the minimum value of the Coronary development factor. Then the stem crown production is called the divergence phase, and the other capillaries’ growth is known as the clip phase. Based on the arteries leader’s coronary development factor (CDF), the most excellent capillary leader’s (BCL) growth will be there. With and without L-index (voltage stability), HTO and SCCS algorithm’s validity are verified in IEEE 30 bus system. Power loss minimized, voltage deviation also reduced, and voltage stability index augmented.

Keywords: optimal reactive power, transmission loss, heat transfer, simulated coronary circulation system.

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, http://dx.doi.org/10.1109/59.736232.
  6. 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, https://doi.org/10.16984/saufenbilder.421351.
  7. Kamel, S., Abdel-Fatah, S., Ebeed, M., Yu, J., Xie, K., Zhao, C. (2019). Solving optimal reactive power dispatch problem considering load uncertainty. 2019 IEEE Innovative Smart Grid Technologies – Asia (ISGT Asia), 2019, pp. 1335–1340, https://doi.org/10.1109/ISGT-Asia.2019.8881322.
  8. Bhattacharyya, B., Karmakar, N. (2020). Optimal reactive power management problem: A solution using evolutionary algorithms. IETE Technical Review, Vol. 37(5), pp. 540–548, doi: 10.1080/02564602.2019.1675541.
  9. Kamel, S., Abdel-Fatah, S., Ebeed, M., Yu, J., Xie, K., Zhao, C. (2019). Solving optimal reactive power dispatch problem considering load uncertainty. 2019 IEEE Innovative Smart Grid Technologies – Asia (ISGT Asia), Chengdu, China, 2019, pp. 1335–1340, doi: 10.1109/ISGT-Asia.2019.8881322.
  10. 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, https://doi.org/10.3390/en12122333.
  11. 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, https://doi.org/10.5281/zenodo.2585477.
  12. Nguyen, T. T., Vo, D. N. (2020). Improved social spider optimization algorithm for optimal reactive power dispatch problem with different objectives. Neural Computing and Applications, Vol. 32(10), https://doi.org/10.1007/s00521-019-04073-4.
  13. Yang, S., Wang, W., Liu, C., Huang, Y. (2015). Optimal reactive power dispatch of wind power plant cluster considering static voltage stability for low-carbon power system. J. Mod. Power Syst. Clean Energy, Vol. 3(1), pp. 114–122, https://doi.org/10.1007/s40565-014-0091-x.
  14. Emiroglu, S., Uyaroglu, Y., Ozdemir, G. (2017). Distributed reactive power control based conservation voltage reduction in active distribution systems. Advances in Electrical and Computer Engineering, Vol. 17(4), pp. 99–106, doi: 10.4316/AECE.2017.04012.
  15. Ghasemi, M., Taghizadeh, M., Ghavidel, S., Aghaei, J., Abbasian, A. (2015). Solving optimal reactive power dispatch problem using a novel teaching–learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, Vol. 39, pp. 100–108.
  16. Wei, Y.-L., Nguyen, T. T., Vo, D. N., Van Tran, H., Van Dai, L. (2019). Optimal dispatch of reactive power using modified stochastic fractal search algorithm. Complexity, Vol. 2019, https://doi.org/10.1155/2019/4670820.
  17. Padilha-Feltrin, A., Rodezno, D. A. Q., Mantovani, J. R. S. (2015). Volt-VAR multiobjective optimization to peak-load relief and energy efficiency in distribution networks. IEEE Transactions on Power Delivery, Vol. 30(2), pp. 618–626.
  18. Khan, I., Li, Z., Xu, Y., Gu, W. (2016). Distributed control algorithm for optimal reactive power control in power grids. International Journal of Electrical Power & Energy Systems, Vol. 83, pp. 505–513.
  19. Castillo, A., Lipka, P., Watson, J.-P., Oren, S. S., O’Neill, R. P. (2016). A successive linear programming approach to solving the IV-ACOPF. IEEE Transactions on Power Systems, Vol. 31(4), pp. 2752–2763.
  20. Olabode, O. E., Okakwu, I. K., Alayande, A. S., Ajewole, T. O. (2020). A two‐stage approach to shunt capacitor‐based optimal reactive power compensation using loss sensitivity factor and cuckoo search algorithm. Energy Storage, Vol. 2, https://doi.org/10.1002/est2.122.
  21. Bhattacharyya ,B., Raj, S. (2017). Differential evolution technique for the optimization of reactive power reserves. Journal of Circuits, Systems and Computers, Vol. 26(10), https://doi.org/10.1142/S0218126617501559.
  22. Dutta, S., Roy, P. K., Nandi, D. (2016). Optimal location of STATCOM using chemical reaction optimization for reactive power dispatch problem. Ain Shams Engineering Journal, Vol. 7(1), pp. 233–247, https://doi.org/10.1016/j.asej.2015.04.013.
  23. Cong, Z., Haoyong, C., Honwing, N., Zipeng, L., Manlan, G., Dong, H., Solution of reactive power optimization including interval uncertainty using genetic algorithm. IET Generation Transmission Distribution, Vol. 11(5), pp. 3657–3664.
  24. Valipour, K., Ghasemi, A. (2015). Using a new modified harmony search algorithm to solve multi-objective reactive power dispatch in deterministic and stochastic models. Journal of AI and Data Mining, Vol. 5(1), pp. 89–100.
  25. Baziareh, A., Kavousi-Fard, F., Zare, A., Abasizade, A., Saleh, S. (2015). Stochastic reactive power planning in distribution systems considering wind turbines electric power variations’. IOS Press Content Library, Vol. Jan.2015, pp. 1081–1087.
  26. 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, pp. 46–64, https://doi:10.4018/978-1-5225-6971-8.ch002.
  27. Nagendra, P., Halder Nee Dey, S., Paul, S. (2014). Voltage stability assessment of a power system incorporating FACTS controllers using unique network equivalent. Ain Shams Engineering Journal, Vol. 5(1), pp. 103–111.
  28. Nagendra, P., Halder Nee Dey, S. and 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.
  29. 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), https://doi.org/10.1371/journal.pone.0185454.
  30. Li, J., Wang, N., Zhou, D., Hu, W., Huang, Q., Chen, Z., Blaabjerg, F. (2020). Optimal reactive power dispatch of permanent magnet synchronous generator-based wind farm considering levelised production cost minimization. Renewable Energy, Vol. 145, pp. 1–12.
  31. Prasad, C. D., Kumar, G. P. (2015). 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, pp. 64–68.
  32. Morsal, J., Zare, K., Hagh, M. T. (2015). Performance Comparison of TCSC with TCPS and SSSC Controllers in AGC of Realistic Interconnected Multi-Sources Power System. Elsevier.
  33. Fadel, W., Kilic, U., Ayan, K. (2021). Optimal reactive power flow of power systems with two-terminal HVDC and multi distributed generations using backtracking search algorithm. International Journal of Electrical Power & Energy Systems, Vol. 127, 106667, https://doi.org/10.1016/j.ijepes.2020.106667.
  34. Wei, H., Lin, C., 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.
  35. 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, https://doi.org/10.1002/etep.2269.
  36. 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.
  37. Du, Z., Nie, Y., Liao, P. (2014). PCPDIPM‐based optimal reactive power flow model using augmented rectangular coordinates. Int. Trans. Electr. Energ. Syst., Vol. 24, pp. 597–608.
  38. Liu, B., Liu, F., Zhai, B., Lan, H. (2019). Investigating continuous power flow solutions of IEEE 14‐bus system. IEEJ Trans Elec Electron Eng, Vol. 14, pp. 157–159.
  39. 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.
  40. Illinois Center for a Smarter Electric Grid (ICSEG). Available online: https://icseg.iti.illinois.edu/ieee-30-bussystem.
  41. 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.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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.
  47. 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.
  48. Mahadevan, K., Kannan, P. S. (2010). Comprehensive learning particle swarm optimization for reactive power dispatch. Appl. Soft Comput., Vol. 10(2), pp. 641–652.
  49. 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.
  50. Reddy, S. S. (2014). Faster evolutionary algorithm based optimal power flow using incremental variables. Electrical Power and Energy Systems, Vol. 54, pp. 198–210.
  51. Saddique, M. S., Bhatti, A. R., Haroon, S. S., Sattar, M. K., Amin, S., Sajjad, A. I., ul Haq, S. S., Awan, A. B., Rasheed, N. (2020). Solution to optimal reactive power dispatch in transmission system using meta-heuristic techniques – Status and technological review. Electr. Power Syst. Res., Vol. 178, 106031.
  52. Packiasudha, M., Suja, S., Jerome, J. (2017). A new cumulative gravitational search algorithm for optimal allocation of FACT device to minimize system loss in deregulated electrical power environment. Int. J. Electr. Power Energy Syst., Vol. 84, pp. 34–46.
  53. Sahli, Z., Hamouda, A., Bekrar, A., Trentesaux, D. (2014). Hybrid PSO-tabu search for the optimal reactive power dispatch problem. Proceedings of the IECON 2014-40th Annual Conference of the IEEE Industrial Electronics Society, pp. 3536–3542, doi: 10.1109/IECON.2014.7049024.
  54. Mouassa, S., Bouktir, T., Salhi, A. (2017). Ant lion optimizer for solving optimal reactive power dispatch problem in power systems. Engineering Science and Technology, an International Journal, Vol. 20(3), pp. 885–895.
  55. Mandal, B., Roy, P. K. (2013). Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization. International Journal of Electrical Power & Energy Systems, Vol. 53, pp. 123–134.
  56. Khazali, H., Kalantar, M. (2011). Optimal reactive power dispatch based on harmony search algorithm. International Journal of Electrical Power & Energy Systems, Vol. 33(3), pp. 684–692.
  57. Tran, H. V., Pham, T. V., Pham, L. H., Le, N. T., Nguyen, T. T. (2019). Finding optimal reactive power dispatch solutions by using a novel improved stochastic fractal search optimization algorithm. Telecommunication Computing Electronics and Control, Vol. 17(5), pp. 2517–2526.
  58. Polprasert, J., Ongsakul, W., Dieu, V. N. (2016). Optimal reactive power dispatch using improved pseudo-gradient search particle swarm optimization. Electric Power Components and Systems, Vol. 44(5), pp. 518–532.
  59. Duong, T. H., Duong, M. Q., Phan, V.-D., Nguyen, T. T. (2020). Optimal reactive power flow for large-scale power systems using an effective metaheuristic algorithm. Journal of Electrical and Computer Engineering, Vol. 2020, https://doi.org/10.1155/2020/6382507.
  60. MATPOWER 4.1 IEEE 30-Bus and 118-Bus Test System. Retrieved from: http://www.pserc.cornell.edu/matpower.

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