n the safety and economic point of view, Reactive Power is the most problematic thing during the operation of the electrical power system network. Reactive Power supply completion is a nonlinear and has both equality and inequality constraints. In this work, to find the solution of reactive power supply issue, Particle Swarm Optimization (PSO) algorithm and MATPOWER 5.1 toolbox are utilized. PSO is an excellent optimization technique that is also having effective finding ability. One of the best asset of PSO is that the ability of PSO is less sensitive to the complication of the objective function. MAT POWER 5.1 is an open source MATLAB toolbox concentrating on finding the power flow issues. The proposed method in this paper diminishes the active power loss in the conventional power system and determines the optimal location of a new installed Distributed Generator (DG). The IEEE 14 bus system is utilized to find the performance and test results show the perfectness of the proposed method.
reactive power, Particle Swarm Optimization (PSO), matpowr 5.1, Distributed Generator (DG), real power loss
 Yoshida H, Kawata K, Fukuyama Y, Takayama S, Nakanishi Y. (2000). A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Transactions on Power Systems 15: 1232–1239. https://doi.org/10.1109/TPAS.1968.292150
 Das DB, Patvardhan C. (2002). Reactive power dispatch with a hybrid stochastic search technique. International Journal of Electrical Power and Energy Systems 24(9): 731-736. https://doi.org/10.1016/S0142-0615(01)00085-0
 Martinez-Rojas M, Sumper A, Gomis-Bellmunt O, Sudrià AA. (2011). Reactive power dispatch in wind farms using particle swarm optimization technique and feasible solutions search. Applied Energy 88(12): 4678–4686. https://doi.org/10.1016/j.apenergy.2011.06.010
 Amrane Y, Boudour M, Ladjici AA, Elmaouhab A. (2015). Optimal VAR control for real power loss minimization using differential evolution algorithm. International Journal of Electrical Power and Energy Systems 66: 262–271. https://doi.org/10.1016/j.ijepes.2014.10.018
 Hong YY, Lin FJ, Lin YC, Hsu FY. (2014). Chaotic PSO-based var control considering renewables using fast probabilistic power flow. IEEE Transactions on Power Delivery 29: 1666–1674. https://doi.org/10.1109/TPWRD.2013.2285923
 Aggelos, Bouhouras S, Kallisthenis, Sgouras I, Paschalis A, Gkaidatzis, Labridis DP. (2016). Optimal active and reactive nodal power requirements towards loss minimization under reverse power ﬂow constraint deﬁning DG type. International Journal of Electrical Power and Energy Systems 78: 445–454. https://doi.org/10.1016/j.ijepes.2015.12.014
 Kanna B, Singh SN. (2015). Towards reactive power dispatch within a wind farm using hybrid PSO. International Journal of Electrical Power and Energy Systems 69: 232–240. https://doi.org/10.1016/j.ijepes.2015.01.021
 Acharya N, Mahat P, Mithulananthan N. (2006). An analytical approach for DG allocation in primary distribution network. International Journal of Electrical Power and Energy Systems 28(10): 669–678. https://doi.org/10.1016/j.ijepes.2006.02.013
 Zhao B, Guo C, Cao Y. (2005). A multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Transactions on Power Systems 20: 1070–1078. https://doi.org/10.1109/TPWRS.2005.846064
 Singh RP, Mukherjee V, Ghoshal S. (2015). Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers. Applied Soft Computing 29: 298–309. https://doi.org/10.1016/j.asoc.2015.01.006
 Kansal S, Kumar V, Tyagi B. (2013). Optimal placement of different type of DG sources in distribution networks. International Journal Electrical Power and Energy Systems 53: 752-760. https://doi.org/10.1016/j.ijepes.2013.05.040
 Srivastava L, Singh H. (2015). Hybrid multi-swarm particle swarm optimisation based multi-objective reactive power dispatch. IET Generation, Transmission and Distribution 9(8): 727–739. https://doi.org/10.1049/iet-gtd.2014.0469
 Leeton U, Uthitsunthorn D, Kwannetr U, Sinsuphun N, Kulworawanichpong T. (2010). 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.
 Gaing ZL. (2003). Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Transactions on Power Systems 18(3): 1187–1195. https://doi.org/10.1109/TPWRS.2003.814889
 Vlachogiannis J, Lee K. (2006). A comparative study on particle swarm optimization for optimal steady-state performance of power systems. IEEE Transactions on Power Systems 21: 1718–1728. https://doi.org/10.1109/TPWRS.2006.883687
 Mishra R, Tapas K, Saha. (2018). Operation in distributed power generation scheme with transition of control between stand-alone and grid connected modes. Modelling, Measurement and Control A 91(2): 48-53. https://doi.org/10.18280/mmc_a.910203
 Kennedy J, Eberhar RC. (1995). Particle swarm optimization. Proceedings of IEEE Conference on Neural Networks, IV, Piscataway, NJ 1942-1948.