Comparative Performance Analysis of Variants of Particle Swarm Optimization of Optimal Reactive Power Dispatch

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Tanmay Das
Ranjit Roy
Kamal Krishna Mandal

Abstract

The Optimal Reactive Power Dispatch (ORPD) is non-linear problem and is a very effective tool in modern power system for designing a more secure and economic system. It has control variables, which are a combination of continuous and discrete and helps in obtaining the most optimized result satisfying all the equality and inequality constraints. The results obtained not only reduces the real power losses of the system but also helps in restricting the voltage deviation to a much greater extent and thus maintaining the stability of the entire system. In this paper, the ORPD problem is solved as a single objective problem with two different objectives like minimization of real power loss and minimization of voltage deviation. Here, four different variants of PSO are used to solve the problem and the results are compared. The algorithms considered in this paper are tested on IEEE 30 bus and IEEE 57 bus system.

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How to Cite
Das, T., Roy, R., & Mandal, K. K. (2019). Comparative Performance Analysis of Variants of Particle Swarm Optimization of Optimal Reactive Power Dispatch. Power Research - A Journal of CPRI, 16–24. https://doi.org/10.33686/pwj.v15i1.144733

References

  1. Wu QH, Cao YJ, Wen JY. Optimal reactive power dispatch using an adaptive genetic algorithm. International Journal of Electrical Power and Energy Systems. 1998; 20(8):563–9. https://doi.org/10.1016/S0142-0615(98)00016-7
  2. Huang CM, Huang YC. Combined differential evolution algorithm and ant system for optimal reactive power dispatch. Energy Procedia. 2012; 14:1238–43. https://doi.org/10.1016/j.egypro.2011.12.1082
  3. Ramirez JM, Gonzalez JM, Ruben TO. An investigation about the impact of the optimal reactive power dispatch solved by DE. International Journal of Electrical Power & Energy Systems. 2011; 33(2):236–44. https://doi.org/10.1016/j.ijepes.2010.08.019
  4. Das T, Roy R. Optimal reactive power dispatch using JAYA algorithm. Emerging Trends in Electronic Devices and Computational Techniques (EDCT); 2018. p. 1–6. https://doi.org/10.1109/EDCT.2018.8405071
  5. Rajan A, Malakar T. Optimal reactive power dispatch using hybrid Nelder-Mead simplex based firefly algorithm. International Journal of Electrical Power & Energy Systems. 2015; 66:9–24. https://doi.org/10.1016/j.ijepes.2014.10.041
  6. Eberhart R, Kennedy J. Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks. 1995; 4:1942–8.
  7. Ghatak SR, Sannigrahi S, Acharjee P. Comparative performance analysis of DG and DSTATCOM using improved PSO based on success rate for deregulated environment. IEEE Systems Journal. 2017; 12(3):2791–802. https://doi.org/10.1109/JSYST.2017.2691759
  8. Washington University [Internet]. Available from: https://www2.ee.washington.edu/research/pstca/