DocumentCode :
2136504
Title :
The Available Transfer Capability based on a chaos cloud particle swarm algorithm
Author :
Hongsheng Su ; Ying Qi ; Xi Song
Author_Institution :
Inst. of Autom. & Electr. Eng., Lanzhou Jiaotong Univ., Lanzhou, China
fYear :
2013
fDate :
23-25 July 2013
Firstpage :
574
Lastpage :
579
Abstract :
An optimal power flow model was established for Available Transfer Capability (ATC) under the static security constraints. The maximum active power of all load nodes in receiving area was taken as objective function. To aim at the low accuracy and premature convergent in ATC optimization algorithms, the chaos cloud particle swarm algorithm based on golden section evaluation criteria (CCGPSO) was proposed. This method divided the particle swarm into standard particle, chaos cloud particle and cloud particle, which used the golden section judge principle according to fitness level. Every sub-swarm particle had respective different algorithm operations. The ATC calculated by the proposed algorithm was simulated in the IEEE-30 node test system. Results are compared with the cloud PSO and chaos PSO algorithm. The simulation results verify that the CCGPSO is greatly superior to the cloud PSO and chaos PSO in terms of accuracy and speed. It is more suitable for solving such large-scale non-linear multi-constraint optimization problems.
Keywords :
cloud computing; nonlinear programming; particle swarm optimisation; security of data; ATC optimization algorithms; available transfer capability; chaos PSO algorithm; chaos cloud particle swarm algorithm; cloud PSO; cloud particle swarm algorithm; golden section judge principle; load nodes; nonlinear multiconstraint optimization problems; objective function; optimal power flow model; static security constraints; Accuracy; Chaos; Convergence; Load flow; Mathematical model; Optimization; Particle swarm optimization; Available Transfer Capability(ATC); golden section; particle swarm optimization algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2013 Ninth International Conference on
Conference_Location :
Shenyang
Type :
conf
DOI :
10.1109/ICNC.2013.6818042
Filename :
6818042
Link To Document :
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