Document Type : Research Paper
Author
Department of EEE Prasad V.Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh-520007.
Abstract
In this work, Greenland Wolf Optimization (GW) algorithm has been applied for real power loss reduction. Natural actions of the Greenland wolf have been mimicked to design the GW algorithm. Greenland wolf found in North West of green land and typical size of the pack is three. Arctic hares, musk oxen, and lemmings are main prey for green land wolf and they migrate with respect to availability of food resources. Through flag vector, position, and velocity updating property Exploration, Exploitation capability of the algorithm has been enhanced. Proposed GW algorithm has been tested in standard IEEE 118 bus test system and results show the best performance of the GW algorithm in reducing the real power loss efficiently.
Keywords
Main Subjects
[1] Lee, K. Y., Park, Y. M., & Ortiz, J. L. (1984, May). Fuel-cost minimisation for both real-and reactive-power dispatches. IEE proceedings C (generation, transmission and distribution) (Vol. 131, No. 3, pp. 85-93). IET Digital Library.
[2] Aoki, K., Nishikori, A., & Yokoyama, R. (1987). Constrained load flow using recursive quadratic programming. IEEE transactions on power systems, 2(1), 8-16.
[3] Kirschen, D. S., & Van Meeteren, H. P. (1988). MW/voltage control in a linear programming based optimal power flow. IEEE transactions on power systems, 3(2), 481-489.
[4] Liu, W. H., Papalexopoulos, A. D., & Tinney, W. F. (1992). Discrete shunt controls in a Newton optimal power flow. IEEE transactions on power systems, 7(4), 1509-1518.
[5] Quintana, V. H., & Santos-Nieto, M. (1989). Reactive-power dispatch by successive quadratic programming. IEEE transactions on energy conversion, 4(3), 425-435.
[6] De Sousa, V. A., Baptista, E. C., & Da Costa, G. R. M. (2012). Optimal reactive power flow via the modified barrier Lagrangian function approach. Electric power systems research, 84(1), 159-164.
[7] Li, Y., Li, X., & Li, Z. (2017). Reactive power optimization using hybrid CABC-DE algorithm. Electric power components and systems, 45(9), 980-989.
[8] 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, 46-64.
[9] 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, 34(6), 4684-4693.
[10] Prasad, D., & Mukherjee, V. (2018). Solution of optimal reactive power dispatch by symbiotic organism search algorithm incorporating FACTS devices. IETE journal of research, 64(1), 149-160.
[11] 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, 12(12), 2333.
[12] Mahate, R. K., & Singh, H. (2019). Multi-objective optimal reactive power dispatch using differential evolution. International journal of engineering technologies and management research, 6(2), 27-38.
[13] Yalçın, E., Taplamacıoğlu, M. C., & Çam, E. (2019). The adaptive chaotic symbiotic organisms search algorithm proposal for optimal reactive power dispatch problem in power systems. Electrica, 19(1), 37-47.
[14] 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, 38(1), 304-324. https://doi.org/10.1108/COMPEL-05-2018-0208.
[15] 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, 12(12), 2333.
[16] Chen, G., Liu, L., Zhang, Z., & Huang, S. (2017). Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints. Applied soft computing, 50, 58-70.
[17] Yamany, W., Emary, E., & Hassanien, A. E. (2014, December). Wolf search algorithm for attribute reduction in classification. 2014 IEEE symposium on computational intelligence and data mining (CIDM) (pp. 351-358). IEEE.
[18] UW Department of Electrical & Computer Engineering. (n.d.). Retrieved from https://www.ece.uw.edu/trsearch/pstca
[19] 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, 15(8), 316-327.
[20] Duong, T. L., 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. https://doi.org/10.1155/2020/6382507
[21] Van Tran, H., Van Pham, T., 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. Telkomnika, 17(5), 2517-2526.
[22] Saddique, M. S., Bhatti, A. R., Haroon, S. S., Sattar, M. K., Amin, S., Sajjad, I. A., ... & Rasheed, N. (2020). Solution to optimal reactive power dispatch in transmission system using meta-heuristic techniques―Status and technological review. Electric power systems research, 178, 106031. https://doi.org/10.1016/j.epsr.2019.106031
[23] Mugemanyi, S., Qu, Z., Rugema, F. X., Dong, Y., Bananeza, C., & Wang, L. (2020). Optimal Reactive Power Dispatch Using Chaotic Bat Algorithm. IEEE Access, 8, 65830-65867. doi: 10.1109/ACCESS.2020.2982988
[24] Marquard-Petersen, U. (2011). Invasion of eastern Greenland by the high arctic wolf Canis lupus arctos. Wildlife biology, 17(4), 383-388.
[25] Jafari, H., Ehsanifar, M., & Sheykhan, A. (2020). Finding optimum facility’s layout by developed simulated annealing algorithm. International journal of research in industrial engineering, 9(2), 172-182.
[26] Ejlaly, B., Bagheri, S. F., & Ghaziani, K. (2019). Integrated and Periodic Relief Logistics Planning for Reaction Phase in Uncertainty Condition and Model Solving by Particles Swarm Optimization Algorithm. Int. J. Res. Ind. Eng, 8(4), 294-311. doi: 10.22105/riej.2020.219060.1120.
[27] Moradi, N., & Shadrokh, S. (2019). A simulated annealing optimization algorithm for equal and un-equal area construction site layout problem. International journal of research in industrial engineering, 8(2), 89-104.
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