Open accessResearch articleFirst published online 2020-1
RETRACTED: Pareto-based allocations of multi-type flexible AC transmission system devices for optimal reactive power dispatch using Kinetic Gas Molecule Optimization algorithm
At the request of the Publisher, the following article has been retracted:
Panthagani P, Rao RS. Pareto-based allocations of multi-type flexible AC transmission system devices for optimal reactive power dispatch using Kinetic Gas Molecule Optimization algorithm. Measurement and Control. 2020;53(1-2):239-249. doi:10.1177/0020294019866857
The article has been retracted due to concerns over the integrity of the peer review process and the involvement of an unauthorised third party. The corresponding author has explained that they used a third-party for publication assistance.
Adhering to the international guidelines established by the Committee on Publication Ethics, the Journal Editor has determined these are grounds for retraction.
The authors did not agree with the retraction.
References
1.
ShareefSKMSrinivasa RaoR. Optimal reactive power dispatch under unbalanced conditions using hybrid swarm intelligence. Comput Electr Eng2018; 69: 183–193.
2.
MouassaSBouktirTSalhiA. Ant lion optimizer for solving optimal reactive power dispatch problem in power systems. Eng Sci Technol Int J2017; 20(3): 885–895.
3.
SinghRPMukherjeeVGhoshalSP. Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers. Appl Soft Comput2015; 29: 298–309.
4.
ZhangCChenHXuX, et al. Pareto front of multi-objective optimal reactive power dispatch. In: Proceedings of the 2014 IEEE PES Asia-Pacific power and energy engineering conference (APPEEC), Hong Kong, China, 7–10 December 2014.
5.
MahadevanKKannanPS. Comprehensive learning particle swarm optimization for reactive power dispatch. Appl Soft Comput2010; 10(2): 641–652.
6.
SulaimanMHRashidMMAlimanO, et al. Loss minimisation by optimal reactive power dispatch using cuckoo search algorithm. In: Proceedings of the 3rd IET international conference on clean energy and technology (CEAT), Kuching, Malaysia, 24–26 November 2014, pp. 32–34. New York: IET.
7.
ZhouBChanKWYuT, et al. Strength Pareto multigroup search optimizer for multiobjective optimal reactive power dispatch. IEEE T Ind Inform2014; 10(2): 1012–1022.
8.
NiknamTNarimaniMRAzizipanah-AbarghooeeR, et al. Multiobjective optimal reactive power dispatch and voltage control: a new opposition-based self-adaptive modified gravitational search algorithm. IEEE Syst J2013; 7(4): 742–753.
9.
DuttaSRoyPKMannaDK. HBBO optimization for optimal reactive power dispatch incorporating TCSC and TCPS devices. In: Michael Faraday IET international summit 2015, Kolkata, India, 12–13 September 2015, pp. 27–28. New York: IET.
10.
ShrawaneSSDiagavaneMBawaneN. Concoction of UPFC for optimal reactive power dispatch using hybrid GAPSO approach for power loss minimisation. In: Proceedings of the 2014 6th IEEE power India international conference (PIICON), New Delhi, India, 5–7 December 2014.
11.
JordehiAR. Optimal allocation of FACTS devices for static security enhancement in power systems via imperialistic competitive algorithm (ICA). Appl Soft Comput2016; 48: 317–328.
12.
CanbingLILiwuXIAOYijiaCAO, et al. Optimal allocation of multi-type FACTS devices in power systems based on power flow entropy. J Mod Power Syst Cle2014; 2(2): 173–180.
13.
Jyotshna DeviKMadhuriN. Optimal allocation of SVC for enhancement of voltage stability using harmony search algorithm. Int J Adv Res Electr Electron Instrum Eng2015; 4(7): 6693–6701.
14.
DuttaSPaulSKumar RoyP. Optimal allocation of SVC and TCSC using quasi-oppositional chemical reaction optimization for solving multi-objective ORPD problem. J Electr Syst Inf Technol2018; 5: 83–98.
15.
AgrawalRBharadwajSKKothariDP. Population based evolutionary optimization techniques for optimal allocation and sizing of thyristor controlled series capacitor. J Electr Syst Inf Technol2018; 5: 484–501.
16.
HaghMTAlipourMTeimourzadehS. Application of HGSO to security based optimal placement and parameter setting of UPFC. Energ Convers Manage2014; 86: 873–885.
17.
MoeinSLogeswaranR. KGMO: a swarm optimization algorithm based on the kinetic energy of gas molecules. Inform Sci2014; 275: 127–144.
18.
BasuM. Multi-objective optimal reactive power dispatch using multi-objective differential evolution. Int J Elec Power2016; 82: 213–224.
19.
El ElaAAAAbidoMASpeaSR. Differential evolution algorithm for optimal reactive power dispatch. Electr Pow Syst Res2011; 81(2): 458–464.
