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A novel merchant optimization algorithm for solving optimal reactive power problem

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Języki publikacji
EN
Abstrakty
EN
In this paper Merchant Optimization Algorithm (MOA) is proposed to solve the optimal reactive power problem. Projected algorithm is modeled based on the behavior of merchants who gain in the market through various mode and operations. Grouping of the traders will be done based on their specific properties, and by number of candidate solution will be computed to individual merchant. First Group named as “Ruler candidate solution” afterwards its variable values are dispersed to the one more candidate solution and it named as “Serf candidate solution” In standard IEEE 14, 30, 57 bus test systems Merchant Optimization Algorithm (MOA) have been evaluated. Results show the proposed algorithm reduced power loss effectively.
Twórcy
  • 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”, IEE Proceedings C (Generation, Transmission and Distribution), vol. 131, no. 3, 1984, 85-93, 10.1049/ip-c.1984.0012.
  • [2] N. I. Deeb and S. M. Shahidehpour, “An Efficient Technique for Reactive Power Dispatch Using Revised Linear Programming Approach”, Electric Power Systems Research, vol. 15, no. 2, 1988, 21-134, 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, 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, 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, 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, 10.1016/j.asoc.2017.05.057.
  • [7] G. Chen, L. Liu, Z. Zhang and S. Huang, “Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints”, Applied Soft Computing, vol. 50, 2017, 58-70, 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 large-scale optimal reactive power dispatch”, Applied Soft Computing, vol. 53, 2017, 441-456, 10.1016/j.asoc.2017.01.012.
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  • [10] M. Mahaletchumi, A. Nor Rul Hasma, S. M. H., 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”, Journal of Electrical Systems - Special Issue AMPE2015, vol. 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, 1-8, 10.1109/IPACT.2017.8244970.
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  • [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, 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, 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, 10.1007/s12293-016-0212-3.
  • [17] “Power Systems Test Case Archive”. University of Washington, Electrical & Computer Engineering, https://labs.ece.uw.edu/pstca/. Accessed on: 2021-06-28.
  • [18] 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 Technology, vol. 15, no. 8, 2018, 316-327, 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 (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-706a9846-ee9c-4ac7-8bae-c39e17facec0
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