Raising Power Quality and Improving Reliability by Distribution Network Reconfiguration in the Presence of Renewable Energy Sources
Subject Areas : journal of Artificial Intelligence in Electrical Engineering
Mohamad Taghi Babajani BaghmisheZad
1
,
Hosein NasirAghdam
2
1 -
2 -
Keywords:
Abstract :
[1]T.Niknam and E.Azad Farsani (2010). A hybrid
self-adaptive particle swarm optimization and
modified shuffled frog leaping algorithm for
distribution feeder reconfiguration, Engin Applic
Artific Intellgn, vol. 23, no. 8, pp.1340-1349.
[2] J.Olamaei, T.Niknam, S.Badali, and A. Arefi
(2012). Distribution feeder reconfiguration for
loss minimization based on modified honey bee
mating optimization algorithm, Energ Policy,
vol. 14, pp.304-311.
[3] L. W.De Oliveira, E.J. De Oliveira, F.V. Gomes,
S I.C.ilva, A. L.M.Marcato, and P.V.C. Resende,
(2014). Artificial immune systems applied to the
reconfiguration of electrical power distribution
networks for energy loss minimization, Int J Elec
Power, vol. 56, pp.64-74.
[4] J E.osé de Oliveira, G.José Rosseti, L.Willer de
Oliveira, F.Vanderson Gomes, and W.Peres
(2014). New algorithm for reconfiguration and
operating procedures in electric distribution
systems, Int J Elec Power, vol. 57, pp.129-134.
[5] C.H.Nogueira de Resende Barbosa, M.H. Soares
Mendes, and J.Antônio de Vasconcelos (2014).
Robust feeder reconfiguration in radial
distribution networks, Int J Elec Power, vol. 54,
pp.619-630.
[6] Ch.T.Su, Ch.F.Chang, and J.P.Chiou (2005).
Distribution network reconfiguration for loss
reduction by ant colony search algorithm, Int J
Elec Power, vol. 75, pp.190-199.
[7] A.Kavousi-Fard, and M.R.Akbari-Zadeh (2013).
Reliability enhancement using optimal
distribution feeder reconfiguration,
Neurocomput, vol. 106, pp.1-11.
[8] J.P.Chiou, Ch.F.Chang, and Ch.-T. Su (2005).
Variable scaling hybrid differential evolution for
solving network reconfiguration of distribution
systems, IEEE T Power Syst, vol. 20, no. 2,
pp.668-674.
[9] J.Mendoza, R.López, D.Morales, E.López,
P.Dessante, and R.Moraga (2006). Minimal loss
reconfiguration using genetic algorithms with
restricted population and addressed operators:
real application, IEEE T Power Syst, vol. 21, no.
2, pp.948-954.
[10] S.H.Mirhoseini, S.M. Hosseini, M.Ghanbari, and
M.Ahmadi (2014). A new improved adaptive
imperialist competitive algorithm to solve the
reconfiguration problem of distribution systems
for loss reduction and voltage profile
improvement, Int J Elec Power, vol. 55, pp.128-
143.
[11] A.E. Milani, and M.R.Haghifam (2013). A new
probabilistic approach for distribution network
reconfiguration: Applicability to real networks,
Math Compt Model, vol. 57, pp.169-179.
[12] A.E.Milani, and M.R.Haghifam (2013). An
evolutionary approach for optimal time interval
determination in distribution network
reconfiguration under variable load, Math Compt
Model, vol. 57, pp.68-77.
[13] B.Tomoiaga, M.Chindris, A.Sumper,
R.Villafafila-Robles, and A.Sudria-Andreu
(2013). Distribution system reconfiguration using
genetic algorithm based on connected graphs, Int
J Elec Power, vol. 104, pp.216-225.
[14] A.Kavousi-Fard, and T.Niknam (2014). Multiobjective
stochastic Distribution Feeder
Reconfiguration from the reliability point of
