Optimal Placement of DG for Optimal Reactive Power Dispatch Using PSO Algorithm

  • Authors

    • Dsnmra o
    • Dr.Niranjan Kumar
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.24.21874

  • Abstract

     In the safety and economic point of view, Reactive Power is the most problematic thing during the operation of the electrical network. Reactive Power supply completion has nonlinear, equality and inequality constraints. Proposed work is carried out, to find the solution for reactive power supply issue, Particle Swarm Optimization (PSO) process as well as MATPOWER 5.1 implement package are developed in this process. PSO is an excellent optimization technique that is also having effective finding ability. One of the best assets of PSO is that its capacity is fewer sensitive than complication of the independent function. MAT POWER 5.1 is an undeveloped basis MATLAB implement package, concentrating the power flow issues findings. Suggested technique diminishes active power damage in the conventional power system and decides the optimum location of a newly setup Distributed Generator (DG). The IEEE 14bus arrangement is utilized to find the performance and test outcomes shown the perfectness of the recommended method.

  • References

    1. [1] H. Dommel and W. Tinney. Optimal power flow solutions,†IEEE Transactions on Power Apparatus and Systems. Vol. PAS-87, pp. 1866–1876, Oct 1968.

      [2] H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama, and Y. Nakanishi. A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Transactions on Power Systems, vol. 15, pp. 1232–1239, Nov 2000

      [3] D. B. Das and C. Patvardhan. Reactive power dispatch with a hybrid stochastic search technique. International Journal of Electrical Power and Energy Systems, vol. 24, no. 9, pp. 731 – 736, 2002.

      [4] M. Martinez-Rojas, A. Sumper, O. Gomis-Bellmunt, and A. Sudrià Andreu. Reactive power dispatch in wind farms using particle swarm optimization technique and feasible solutions search. Applied Energy, vol. 88, no. 12, pp. 4678 – 4686, 2011.

      [5] Y. Amrane, M. Boudour, A. A. Ladjici, and A. Elmaouhab. Optimal {VAR} control for real power loss minimization using differential evolution algorithm. International Journal of Electrical Power and Energy Systems, vol. 66, pp. 262 – 271, 2015.

      [6] Y.-Y. Hong, F.-J. Lin, Y.-C. Lin, and F.-Y. Hsu. Chaotic pso-based var control considering renewables using fast probabilistic power flow. IEEE Transactions on Power Delivery, vol. 29, pp. 1666–1674, Aug 2014.

      [7] Aggelos S. Bouhouras, Kallisthenis I. Sgouras, Paschalis A. Gkaidatzis and Dimitris P. Labridis. Optimal active and reactive nodal power requirements towards loss minimization under reverse power flow constraint deï¬ning DG type. International Journal of Electrical Power and Energy Systems, Vol. 78, pp. 445–454, June 2016.

      [8] B. Kanna and S. N. Singh. Towards reactive power dispatch within a wind farm using hybrid pso. International Journal of Electrical Power and Energy Systems, vol. 69, pp. 232 – 240, 2015.

      [9] N. Acharya, P. Mahat, and N. Mithulananthan. An analytical approach for DG allocation in primary distribution network. International Journal of Electrical Power and Energy Systems, vol. 28, no. 10, pp. 669–678, Dec.2006.

      [10] B. Zhao, C. Guo, and Y. Cao. A multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Transactions on Power Systems, vol. 20, pp. 1070–1078, May 2005.

      [11] R. P. Singh, V. Mukherjee, and S. Ghoshal. Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers. Applied Soft Computing, vol. 29, pp. 298 – 309, 2015.

      [12] Satish kansal, Vishal Kumar and BarjeevTyagi. Optimal placement of different type of DG sources in distribution networks. Electrical power and Energy Systems, vol. 53, pp.752-760, 2013.

      [13] L. Srivastava and H. Singh. Hybrid multi-swarm particle swarm optimisation based multi-objective reactive power dispatch,†IET Generation, Transmission Distribution, vol. 9, no. 8, pp. 727–739, 2015.

      [14] Leeton U, Uthitsunthorn D, Kwannetr U, Sinsuphun N, Kulworawanichpong T. Power loss minimization using optimal power flow based on particle swarm optimization. Proceedings of IEEE Conference on Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), pp. 440–444, 2010.

      [15] Z. L. Gaing. Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Transactions on Power Systems, vol. 18, no. 3, pp. 1187–1195, August 2003.

      [16] J. Vlachogiannis and K. Lee. A comparative study on particle swarm optimization for optimal steady-state performance of power systems. IEEE Transactions on Power Systems, vol. 21, pp. 1718–1728, Nov 2006.

      [17] J. Kennedy, R.C. Eberhart. Particle Swarm Optimization. Proceedings of IEEE Conference on Neural Networks, IV, Piscataway, NJ, pp. 1942– 1948,1995.

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  • How to Cite

    o, D., & Kumar, D. (2018). Optimal Placement of DG for Optimal Reactive Power Dispatch Using PSO Algorithm. International Journal of Engineering & Technology, 7(4.24), 137-141. https://doi.org/10.14419/ijet.v7i4.24.21874

    Received date: 2018-11-27

    Accepted date: 2018-11-27

    Published date: 2018-11-27