Journal of Applied Science, Engineering, Technology, and Education
Vol. 3 No. 1 (2021), Pages 12-21 · 2021-06-04
Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm for Solving Optimal Reactive Power Dispatch Problem
- Publication History
- Published online: June 4, 2021
- DOI
- https://doi.org/10.35877/454RI.asci31106
- Copyright
- Copyright (c) 2021 Kanagasabai Lenin (Author)
Abstract
In this work Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm (HGPSOS) has been done for solving the power dispatch problem. Genetic particle swarm optimization problem has been hybridized with Symbiotic organisms search (SOS) algorithm to solve the problem. Genetic particle swarm optimization algorithm is formed by combining the Particle swarm optimization algorithm (PSO) with genetic algorithm (GA). Symbiotic organisms search algorithm is based on the actions between two different organisms in the ecosystem- mutualism, commensalism and parasitism. Exploration process has been instigated capriciously and every organism specifies a solution with fitness value. Projected HGPSOS algorithm improves the quality of the search. Proposed HGPSOS algorithm is tested in IEEE 30, bus test system- power loss minimization, voltage deviation minimization and voltage stability enhancement has been attained.
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References (27)
- K. Y. Lee “Fuel-cost minimisation for both real and reactive-power dispatches,” Proceedings Generation, Transmission and Distribution Conference, vol/issue: 131(3), pp. 85-93, (1984).
- Aoki, K., A. Nishikori and R.T. Yokoyama. Constrained load flow using recursive quadratic programming.IEEE T. Power Syst., 2(1): 8-16.(1987)
- Kirschen, D.S. and H.P. Van Meeteren,. MW/voltage control in a linear programming based optimal power flow. IEEE T. Power Syst., 3(2): 481-489.(1988)
- Liu, W.H.E., A.D. Papalexopoulos and W.F. Tinney. Discrete shunt controls in a Newton optimal power flow. IEEE T. Power Syst., 7(4): 1509-1518.(1992)
- V. H. Quintana and M. Santos-Nieto, “Reactive-power dispatch by successive quadratic programming,” IEEE Transactions on Energy Conversion, vol. 4, no. 3, pp. 425–435, 1989.
- V. de Sousa, E. Baptista, and G. da Costa, “Optimal reactive power flow via the modified barrier Lagrangian function approach,” Electric Power Systems Research, vol. 84, no. 1, pp. 159–164, 2012.
- Mahaletchumi A/P Morgan, Nor Rul Hasma Abdullah, Mohd Herwan Sulaiman,Mahfuzah Mustafa and Rosdiyana Samad.(2016). “Multi-Objective Evolutionary Programming (MOEP) Using Mutation Based on Adaptive Mutation Operator (AMO) Applied For Optimal Reactive Power Dispatch”, ARPN Journal of Engineering and Applied Sciences, VOL. 11, NO. 14.
- Pandiarajan, K. & Babulal, C. K.(2016). “ Fuzzy harmony search algorithm based optimal power flow for power system security enhancement”. International Journal Electric Power Energy Syst., vol. 78, pp. 72-79.
- Mahaletchumi Morgan, Nor Rul Hasma Abdullah, Mohd Herwan Sulaiman, Mahfuzah Mustafa, Rosdiyana Samad.(2016). “Benchmark Studies on Optimal Reactive Power Dispatch (ORPD) Based Multi-objective Evolutionary Programming (MOEP) Using Mutation Based on Adaptive Mutation Adapter (AMO) and Polynomial Mutation Operator (PMO)”, Journal of Electrical Systems, 12-1.
- Rebecca Ng Shin Mei, Mohd Herwan Sulaiman, Zuriani Mustaffa,. (2016). “Ant Lion Optimizer for Optimal Reactive Power Dispatch Solution” , Journal of Electrical Systems, "Special Issue AMPE2015", pp. 68-74.
- Roy, Provas Kumar and Susanta Dutta (2019) "Economic Load Dispatch: Optimal Power Flow and Optimal Reactive Power Dispatch Concept." Optimal Power Flow Using Evolutionary Algorithms. IGI Global, 2019. 46-64. Web. 21 . doi:10.4018/978-1-5225-6971-8.ch002
- Christian Bingane, Miguel F. Anjos, Sébastien Le Digabel, (2019) “Tight-and-cheap conic relaxation for the optimal reactive power dispatch problem”, IEEE Transactions on Power Systems, DOI:10.1109/TPWRS.2019.2912889,arXiv:1810.03040.
- Dharmbir Prasad & Vivekananda Mukherjee (2018) “Solution of Optimal Reactive Power Dispatch by Symbiotic Organism Search Algorithm Incorporating FACTS Devices”, IETE Journal of Research, 64:1, 149-160, DOI: 10.1080/03772063.2017.1334600.
- TM Aljohani, AF Ebrahim, O Mohammed Single (2019) “Multiobjective Optimal Reactive Power Dispatch Based on Hybrid Artificial Physics–Particle Swarm Optimization”,Energies , 12(12),2333; https://doi.org/10.3390/en12122333
- Ram Kishan Mahate, & Himmat Singh. (2019). Multi-Objective Optimal Reactive Power Dispatch Using Differential Evolution. International Journal of Engineering Technologies and Management Research, 6(2), 27–38. http://doi.org/10.5281/zenodo.2585477.
- 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 19, 37-47.
- Mouassa, S. and 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 No. 1, pp. 304-324. https://doi.org/10.1108/COMPEL-05-2018-0208.
- Tawfiq M. Aljohani , Ahmed F. Ebrahim & Osama Mohammed (2019). "Single and Multiobjective Optimal Reactive Power Dispatch Based on Hybrid Artificial Physics–Particle Swarm Optimization," Energies, MDPI, Open Access Journal, vol. 12(12), pages 1-24.
- Ali Nasser Hussain, Ali Abdulabbas Abdullah and Omar Muhammed Neda, ,“Modified Particle Swarm Optimization for Solution of Reactive Power Dispatch”, Research Journal of Applied Sciences, Engineering and Technology 15(8): 316-327, (2018), DOI:10.19026/rjaset.15.5917.
- S. Surender Reddy, “Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm”, International Journal of Electrical and Computer Engineering , Vol. 7, No. 5, pp. 2349-2356. 2017.
- Hao Wang; Wenjian Cai; Youyi Wang , (2017) “Optimization of a hybrid ejector air conditioning system with PSOGA” ,Applied Thermal Engineering, ISSN: 1359-4311, Vol: 112, Page: 1474-1486,
- Behnam Jamali; Mohamad Rasekh; Farnaz Jamadi; Ramin Gandomkar; Faezeh Makiabadi, (2019), “Using PSO-GA algorithm for training artificial neural network to forecast solar space heating system parameters” Applied Thermal Engineering, ISSN: 1359-4311, Vol: 147, Page: 647-660
- S. Duman, Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones, Neural Comput & Applic. 28 (11) (2017) 3571–3585 https://doi.org/10.10 07/s0 0521- 016- 2265- 0 .
- M.-Y. Cheng , C.-K. Chiu , Y.-F. Chiu , Y.-W. Wu , Z.-L. Syu , D. Prayogo , C.-H. Lin , SOS optimization model for bridge life cycle risk evaluation and mainte- nance strategies, J. Chin. Inst. Civil Hydraul. Eng. 26 (4) (2016) 293–308 .
- D.C. Secui , A modified symbiotic organisms search algorithm for large scale economic dispatch problem with valve-point effects, Ener gy 113 (2016) 366–384 .
- Illinois Center for a Smarter Electric Grid (ICSEG). Available online: https://icseg.iti.illinois.edu/ieee-30-bussystem/ (accessed on 25 February 2019).
- Aljohani, T.M.; Ebrahim, A.F.; Mohammed, O. Single and Multiobjective Optimal Reactive Power Dispatch Based on Hybrid Artificial Physics–Particle Swarm Optimization. Energies 2019, 12, 2333.
How to Cite
Lenin, K. (2021). Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm for Solving Optimal Reactive Power Dispatch Problem . Journal of Applied Science, Engineering, Technology, and Education, 3(1), 12–21. https://doi.org/10.35877/454RI.asci31106
