Genetic algorithm based optimization method for reactive power management

F. Oliynyk, A. Kolisnyk, S. Ponomarenko *

Institute of Power Engineering and Control Systems, Lviv Polytechnic National University, Lviv, Ukraine

Abstract

Reactive power management is a complicated and nonlinear problem. With the development of computer-based methods, some new methods have been proposed for this problem. The nature-based optimization algorithms are an efficient way of solving the nonlinear and not differentiable functions. One of the interesting of these algorithms is a genetic algorithm or GA. In this study, we proposed the application of a genetic algorithm for solving reactive power optimization. The proposed method must find the best parameters of power network including the generator node voltage, the transformers tap situation, and the parallel compensators value. In existing electrical power networks, these variables don’t consider and all capacity of the system didn’t use. In the traditional power network, the voltage profile is weak and the active power loss in transmission lines is high. With optimizing the reactive power control parameters, the voltage profile improved and the active power loss will be reduced significantly. In order to test the proposed system, the IEEE standard 25-node network is chosen. The simulation results show that the proposed method has a good effect on system performance.

Keywords

GA, Voltage, Reactive, Generators

Digital Object Identifier (DOI)

https://doi.org/10.21833/AEEE.2019.07.004

Article history

Received 18 February 2019, Received in revised form 15 June 2019, Accepted 15 June 2019

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How to cite

Oliynyk F, Kolisnyk A, and Ponomarenko S (2019). Genetic algorithm based optimization method for reactive power management. Annals of Electrical and Electronic Engineering, 2(7): 16-21

References (37)

  1. Anglada E and Garmendia I (2015). Correlation of thermal mathematical models for thermal control of space vehicles by means of genetic algorithms. Acta Astronautica, 108: 1-17. https://doi.org/10.1016/j.actaastro.2014.11.042   [Google Scholar]
  2. Arya LD, Titare LS, and Kothari DP (2010). Improved particle swarm optimization applied to reactive power reserve maximization. International Journal of Electrical Power and Energy Systems, 32(5): 368-374. https://doi.org/10.1016/j.ijepes.2009.11.007   [Google Scholar]
  3. Balamurugan K, Muralisachithanandam R, and Dharmalingam V (2015). Performance comparison of evolutionary programming and differential evolution approaches for social welfare maximization by placement of multi type FACTS devices in pool electricity market. International Journal of Electrical Power and Energy Systems, 67: 517-528. https://doi.org/10.1016/j.ijepes.2014.12.007   [Google Scholar]
  4. Castoldi MF, Sanches DS, Mansour MR, Bretas NG, and Ramos RA (2014). A hybrid algorithm to tune power oscillation dampers for FACTS devices in power systems. Control Engineering Practice, 24: 25-32. https://doi.org/10.1016/j.conengprac.2013.11.001   [Google Scholar]
  5. Changdar C, Mahapatra GS, and Pal RK (2015). An improved genetic algorithm based approach to solve constrained knapsack problem in fuzzy environment. Expert Systems with Applications, 42(4): 2276-2286. https://doi.org/10.1016/j.eswa.2014.09.006   [Google Scholar]
  6. Chen CH, Liu TK, Chou JH, Tasi CH, and Wang H (2015). Optimization of teacher volunteer transferring problems using greedy genetic algorithms. Expert Systems with Applications, 42(1): 668-678. https://doi.org/10.1016/j.eswa.2014.08.020   [Google Scholar]
  7. Cheng R and Jin Y (2015). A social learning particle swarm optimization algorithm for scalable optimization. Information Sciences, 291: 43-60. https://doi.org/10.1016/j.ins.2014.08.039   [Google Scholar]
  8. Dash P, Saikia LC, and Sinha N (2015). Comparison of performances of several FACTS devices using Cuckoo search algorithm optimized 2DOF controllers in multi-area AGC. International Journal of Electrical Power and Energy Systems, 65: 316-324. https://doi.org/10.1016/j.ijepes.2014.10.015   [Google Scholar]
  9. Dong F, Chowdhury BH, Crow ML, and Acar L (2005). Improving voltage stability by reactive power reserve management. IEEE Transactions on Power Systems, 20(1): 338-345. https://doi.org/10.1109/TPWRS.2004.841241   [Google Scholar]
  10. Duan DL, Ling XD, Wu XY, and Zhong B (2015). Reconfiguration of distribution network for loss reduction and reliability improvement based on an enhanced genetic algorithm. International Journal of Electrical Power and Energy Systems, 64: 88-95. https://doi.org/10.1016/j.ijepes.2014.07.036   [Google Scholar]
  11. Elsheikh A, Helmy Y, Abouelseoud Y, and Elsherif A (2014). Optimal capacitor placement and sizing in radial electric power systems. Alexandria Engineering Journal, 53(4): 809-816. https://doi.org/10.1016/j.aej.2014.09.012   [Google Scholar]
  12. Gasperic S and Mihalic R (2015). The impact of serial controllable FACTS devices on voltage stability. International Journal of Electrical Power and Energy Systems, 64: 1040-1048. https://doi.org/10.1016/j.ijepes.2014.08.010   [Google Scholar]
  13. Gopalakrishnan H and Kosanovic D (2015). Operational planning of combined heat and power plants through genetic algorithms for mixed 0–1 nonlinear programming. Computers and Operations Research, 56: 51-67. https://doi.org/10.1016/j.cor.2014.11.001   [Google Scholar]
  14. He R, Taylor GA, and Song YH (2008). Multi-objective optimal reactive power flow including voltage security and demand profile classification. International Journal of Electrical Power and Energy Systems, 30(5): 327-336. https://doi.org/10.1016/j.ijepes.2007.12.001   [Google Scholar]
  15. Herath MT, Natarajan S, Prusty BG, and John NS (2015). Isogeometric analysis and genetic algorithm for shape-adaptive composite marine propellers. Computer Methods in Applied Mechanics and Engineering, 284: 835-860. https://doi.org/10.1016/j.cma.2014.10.028   [Google Scholar]
  16. Király A and Abonyi J (2015). Redesign of the supply of mobile mechanics based on a novel genetic optimization algorithm using Google maps API. Engineering Applications of Artificial Intelligence, 38: 122-130. https://doi.org/10.1016/j.engappai.2014.10.015   [Google Scholar]
  17. Kumar A and Mittapalli RK (2014). Congestion management with generic load model in hybrid electricity markets with FACTS devices. International Journal of Electrical Power and Energy Systems, 57: 49-63. https://doi.org/10.1016/j.ijepes.2013.11.035   [Google Scholar]
  18. Lee CS, Ayala HVH, and dos Santos Coelho L (2015). Capacitor placement of distribution systems using particle swarm optimization approaches. International Journal of Electrical Power and Energy Systems, 64: 839-851. https://doi.org/10.1016/j.ijepes.2014.07.069   [Google Scholar]
  19. Li Y, Jiao L, Shang R, and Stolkin R (2015). Dynamic-context cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Information Sciences, 294: 408-422. https://doi.org/10.1016/j.ins.2014.10.005   [Google Scholar]
  20. Liu Y, Niu B, and Luo Y (2015). Hybrid learning particle swarm optimizer with genetic disturbance. Neurocomputing, 151: 1237-1247. https://doi.org/10.1016/j.neucom.2014.03.081   [Google Scholar]
  21. Lu HL, Wen XS, Lan L, An YZ, and Li XP (2015). A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops. Journal of Magnetism and Magnetic Materials, 374: 502-507. https://doi.org/10.1016/j.jmmm.2014.08.084   [Google Scholar]
  22. Mukherjee M and Goswami SK (2014). Solving capacitor placement problem considering uncertainty in load variation. International Journal of Electrical Power and Energy Systems, 62: 90-94. https://doi.org/10.1016/j.ijepes.2014.04.004   [Google Scholar]
  23. Nedwick P, Mistr AF, and Croasdale EB (1995). Reactive management a key to survival in the 1990s. IEEE Transactions on Power Systems, 10(2): 1036-1043. https://doi.org/10.1109/59.387949   [Google Scholar]
  24. Quiroz-Castellanos M, Cruz-Reyes L, Torres-Jimenez J, Gómez C, Huacuja HJF, and Alvim AC (2015). A grouping genetic algorithm with controlled gene transmission for the bin packing problem. Computers and Operations Research, 55: 52-64. https://doi.org/10.1016/j.cor.2014.10.010   [Google Scholar]
  25. Shuaib YM, Kalavathi MS, and Rajan CCA (2015). Optimal capacitor placement in radial distribution system using gravitational search algorithm. International Journal of Electrical Power and Energy Systems, 64: 384-397. https://doi.org/10.1016/j.ijepes.2014.07.041   [Google Scholar]
  26. Sreejith S, Simon SP, and Selvan MP (2015). Analysis of FACTS devices on security constrained unit commitment problem. International Journal of Electrical Power and Energy Systems, 66: 280-293. https://doi.org/10.1016/j.ijepes.2014.10.049   [Google Scholar]
  27. Sultana S and Roy PK (2014). Optimal capacitor placement in radial distribution systems using teaching learning based optimization. International Journal of Electrical Power and Energy Systems, 54: 387-398. https://doi.org/10.1016/j.ijepes.2013.07.011   [Google Scholar]
  28. Varadarajan M and Swarup KS (2008). Differential evolutionary algorithm for optimal reactive power dispatch. International Journal of Electrical Power and Energy Systems, 30(8): 435-441. https://doi.org/10.1016/j.ijepes.2008.03.003   [Google Scholar]
  29. Vuletić J and Todorovski M (2014). Optimal capacitor placement in radial distribution systems using clustering based optimization. International Journal of Electrical Power and Energy Systems, 62: 229-236. https://doi.org/10.1016/j.ijepes.2014.05.001   [Google Scholar]
  30. Wang J, Huang W, Ma G, and Chen S (2015). An improved partheno genetic algorithm for multi-objective economic dispatch in cascaded hydropower systems. International Journal of Electrical Power and Energy Systems, 67: 591-597. https://doi.org/10.1016/j.ijepes.2014.12.037   [Google Scholar]
  31. Wu H, Yu CW, Xu N, and Lin XJ (2008). An OPF based approach for assessing the minimal reactive power support for generators in deregulated power systems. International Journal of Electrical Power and Energy Systems, 30(1): 23-30. https://doi.org/10.1016/j.ijepes.2007.06.002   [Google Scholar]
  32. Wu QH, Cao YJ, and Wen JY (1998). Optimal reactive power dispatch using an adaptive genetic algorithm. International Journal of Electrical Power and Energy Systems, 20(8): 563-569. https://doi.org/10.1016/S0142-0615(98)00016-7   [Google Scholar]
  33. Yang N, Yu CW, Wen F, and Chung CY (2007). An investigation of reactive power planning based on chance constrained programming. International Journal of Electrical Power and Energy Systems, 29(9): 650-656. https://doi.org/10.1016/j.ijepes.2006.09.008   [Google Scholar]
  34. Yu W, Li B, Jia H, Zhang M, and Wang D (2015). Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design. Energy and Buildings, 88: 135-143. https://doi.org/10.1016/j.enbuild.2014.11.063   [Google Scholar]
  35. Zhang C, Zheng J, and Zhou Y (2015). Two modified artificial bee colony algorithms inspired by grenade explosion method. Neurocomputing, 151(part3): 1198-1207. https://doi.org/10.1016/j.neucom.2014.04.082   [Google Scholar]
  36. Zhang T, Elkasrawy A, and Venkatesh B (2009). A new computational method for reactive power market clearing. International Journal of Electrical Power and Energy Systems, 31(6): 285-293. https://doi.org/10.1016/j.ijepes.2009.03.015   [Google Scholar]
  37. Zhang X, Chen W, Dai C, and Cai W (2010). Dynamic multi-group self-adaptive differential evolution algorithm for reactive power optimization. International Journal of Electrical Power and Energy Systems, 32(5): 351-357. https://doi.org/10.1016/j.ijepes.2009.11.009   [Google Scholar]