Page 306 - Decision Making Applications in Modern Power Systems

266 Decision Making Applications in Modern Power Systems

system operating conditions and voltage safety margin. These indexes quan-
tify how far the system is from voltage collapse, and how close is each bus
of interest from the instability region.
Finally, a methodology for reactive power dispatch using PSO consider-
ing the tangent-vector for generating selection is proposed. The results show
that an efficient dispatch of reactive power is achieved by the PSO method,
which intelligently determines the adequate contribution of each generating
to minimize system losses. At last, the results for the IEEE 118-bus test sys-
tem indicated that for all scenarios, the active and reactive power margins
increased after the proposed method application.

Acknowledgments
The authors thank to CNPq, CAPES, FAPEMIG, and INERGE for partially supporting this
work. The author Ma´ ıra R. Monteiro is a scholarship holder of CNPq—Brazil. The author
Yuri R. Rodrigues especially thanks CAPES Notice No. 18/2016 of the Full Doctoral
Program Abroad/Process no. 88881.128399/2016-01.

References
[1] R.C. Leme, A.C.Z. Souza, J.C.S. Souza, K.L. Lo, Charging reactive power considering
system security aspects, Int. J. Electr. Power Energy Syst. (2010) 203 4604.
[2] A.C.Z. Souza, Tangent vector applied to voltage collapse and loss sensitivity studies,
Electr. Power Syst. Res. 47 (1) (1998) 65 70.
[3] K. Mahadevana, P.S. Kannanb, Comprehensive learning particle swarm optimization for
reactive power dispatch, Appl. Soft Comput. 10 (2) (2010) 641 652.
[4] M.M. Rojas, A. Sumper, O.G. Bellmunt, A.A. Sundria `, Reactive power dispatch in
wind farms using particle swarm optimization technique and feasible solutions search,
Appl. Energy 88 (12) (2011) 4678 4686.
[5] B. Zhao, C.X. Guo, Y.J. Cao, A multiagent-based particle swarm optimization approach
for optimal reactive power dispatch, IEEE Trans. Power Syst. 20 (2) (2005) 1070 1078.
[6] S. Ganguly, Multi-objective planning for reactive power compensation of radial
distribution networks with unified power quality conditioner allocation using particle
swarm optimization, IEEE Trans. Power Syst. 29 (4) (2014) 1801 1810.
[7] H.T. Yang, J.T. Liao, MF-APSO-based multiobjective optimization for PV system reac-
tive power regulation, IEEE Trans. Sustain. Energy 6 (4) (2015) 1346 1355.
[8] V.N. Costa, M.R. Monteiro, A.C.Z. Souza, Particle swarm optimization applied to reactive
power compensation, in: 17th International Conference on Harmonics and Quality of
Power, Belo Horizonte, 2016.
[9] A.A. Esmin, G.L. Torres, A.C.Z. Souza, A hybrid particle swarm optimization applied to
loss power minimization, IEEE Trans. Power Syst. 20 (2) (2005) 859 866.
[10] L.C.A. Ferreira, A.C.Z. Souza, S. Granville, J.W.M. Lima, Interior point method applied
to voltage collapse problems and losses reduction, IEE Proc. Gener., Transm. Distrib. 149
(2) (2002) 165 170.
[11] P.F. Ribeiro, K. Schlansker, A Hybrid particle swarm and neural network approach for
reactive power control, 2003. Available from: https://www.researchgate.net/publication/
242452972_A_Hybrid_Particle_Swarm_and_Neural_Network_Approach_for_Reactive_
Power_Control.