Timber wolf optimization algorithm for real power loss diminution

• Autorzy: Lenin Kanagasabai

Identyfikatory

DOI

10.14313/JAMRIS/1-2020/3

• Języki publikacji: EN

Abstrakty

EN

In this paper Timber Wolf optimization (TWO) algorithm is proposed to solve optimal reactive power problem. Timber Wolf optimization (TWO) algorithm is modeled based on the social hierarchy and hunting habits of Timber wolf towards finding prey. Based on their fitness values social hierarchy has been replicated by classifying the population of exploration agents. Exploration procedure has been modeled by imitating the hunting actions of timber wolf by using searching, encircling, and attacking the prey. There are three fittest candidate solutions embedded as α, β and γ to lead the population toward capable regions of the exploration space in each iteration of Timber Wolf optimization. Proposed Timber Wolf optimization (TWO) algorithm has been tested in standard IEEE 14, 30 bus test systems and simulation results show the projected algorithm reduced the real power loss efficiently.

Słowa kluczowe

EN

optimal reactive power; transmission loss; Timber Wolf optimization algorithm; TWO algorithm

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2020
• Tom: Vol. 14, No. 1
• Strony: 24--28
• Opis fizyczny: bibliogr. 19 poz., rys.

Twórcy

autor

Lenin Kanagasabai

• Department of Electrical and Electronics Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada, India

Bibliografia

• [1] K. Y. Lee, Y. M. Park and J. L. Ortiz, “Fuel-cost minimisation for both real-and reactive-power dispatches”, Transmission and Distribution IEE Proceedings C - Generation, vol. 131, no. 3, 1984, 85–93, DOI: 10.1049/ip-c.1984.0012.
• [2] N. I. Deeb and S. M. Shahidehpour, “An Efficient Technique for Reactive Power Dispatch Using a Revised Linear Programming Approach”, Electric Power Systems Research, vol. 15, no. 2, 1988, 121–134, DOI: 10.1016/0378-7796(88)90016-8.
• [3] M. Bjelogrlic, M. S. Calovic, P. Ristanovic and B. S. Babic, “Application of Newton’s optimal power flow in voltage/reactive power control”, IEEE Transactions on Power Systems, vol. 5, no. 4, 1990, 1447–1454, DOI: 10.1109/59.99399.
• [4] S. Granville, “Optimal reactive dispatch through interior point methods”, IEEE Transactions on Power Systems, vol. 9, no. 1, 1994, 136–146, DOI: 10.1109/59.317548.
• [5] N. Grudinin, “Reactive power optimization using successive quadratic programming method”, IEEE Transactions on Power Systems, vol. 13, no. 4, 1998, 1219–1225, DOI: 10.1109/59.736232.
• [6] R. Ng Shin Mei, M. H. Sulaiman, Z. Mustaffa and H. Daniyal, “Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique”, Applied Soft Computing, vol. 59, 2017, 210–222, DOI: 10.1016/j.asoc.2017.05.057.
• [7] G. Chen, L. Liu, Z. Zhang and S. Huang, “Optimal reactive power dispatch by improved GSAbased algorithm with the novel strategies to handle constraints”, Applied Soft Computing, vol. 50, 2017, 58–70, DOI: 10.1016/j.asoc.2016.11.008.
• [8] E. Naderi, H. Narimani, M. Fathi and M. R. Narimani, “A novel fuzzy adaptive configuration of particle swarm optimization to solve largescale optimal reactive power dispatch”, Applied Soft Computing, vol. 53, 2017, 441–456, DOI: 10.1016/j.asoc.2017.01.012.
• [9] A. A. Heidari, R. Ali Abbaspour and A. Rezaee Jordehi, “Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems”, Applied Soft Computing, vol. 57, 2017, 657–671, DOI: 10.1016/j.asoc.2017.04.048.
• [10] M. Mahaletchumi, N. R. H. Abdullah, M. H. Sulaiman, M. Mahfuzah and S. Rosdiyana, “Benchmark studies on Optimal Reactive Power Dispatch (ORPD) Based Multi-Objective Evolutionary Programming (MOEP) using Mutation Based on Adaptive Mutation Operator (AMO) and Polynomial Mutation Operator (PMO)”, Journal of Electrical Systems, vol. 12, no. 1, 2016, 121–132.
• [11] R. Ng Shin Mei, M. H. Sulaiman and Z. Mustaffa, “Ant Lion Optimizer for Optimal Reactive Power Dispatch Solution”, International Conference on Advanced Mechanics, Power and (AMPE2015), Journal of Electrical Systems, Special Issue 3, 2015, 68–74.
• [12] P. Anbarasan and T. Jayabarathi, “Optimal reactive power dispatch problem solved by symbiotic organism search algorithm”. In: 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), 2017, 2020–01-08, DOI: 10.1109/IPACT.2017.8244970.
• [13] A. Gagliano and F. Nocera, “Analysis of the performances of electric energy storage in residential applications”, International Journal of Heat and Technology, vol. 35, no. Special Issue 1, 2017, 2020–01-08, DOI: 10.18280/ijht.35Sp0106.
• [14] M. Caldera, P. Ungaro, G. Cammarata and G. Puglisi, “Survey-based analysis of the electrical energy demand in Italian households”, Mathematical Modelling of Engineering Problems, vol. 5, no. 3, 2018, 217–224, DOI: 10.18280/mmep.050313.
• [15] M. Basu, “Quasi-oppositional differential evolution for optimal reactive power dispatch”, International Journal of Electrical Power & Energy Systems, vol. 78, 2016, 29–40, DOI: 10.1016/j.ijepes.2015.11.067.
• [16] G.-G. Wang, “Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems”, Memetic Computing, vol. 10, no. 2, 2018, 151–164, DOI: 10.1007/s12293-016-0212-3.
• [17] L. Li, L. Sun, J. Guo, J. Qi, B. Xu and S. Li, “Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding”, Computational Intelligence and Neuroscience, 2017, 1–16, DOI: 10.1155/2017/3295769.
• [18] “Power Systems Test Case Archive”. University of Washington, Electrical & Computer Engineering – Richard D. Christie, https://labs.ece.uw.edu/pstca/. Accessed on: 2020-05-28.
• [19] A. N. Hussain, A. A. Abdullah and O. M. Neda, “Modified Particle Swarm Optimization for Solution of Reactive Power Dispatch”, Research Journal of Applied Sciences, Engineering and echnology, vol. 15, no. 8, 2018, 316–327, DOI: 10.19026/rjaset.15.5917.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).