DocumentCode :
3109778
Title :
Adaptive clonal selection algorithm for solving OPF problem with emission constraints
Author :
Rao, B.S. ; Vaisakh, K.
Author_Institution :
Dept. of Electr. & Electron. Eng., V.R. Siddhartha Eng. Coll., Vijayawada, India
fYear :
2013
fDate :
13-15 Dec. 2013
Firstpage :
1
Lastpage :
6
Abstract :
This paper presents an artificial immune system (AIS) based adaptive clonal selection algorithm (ACSA) to solve combined economic emission dispatch (EED) problem of thermal units in power system. In this work different emission substances like NOX and SOX are considered along with power demand equality constraints and thermal unit operating limits. The clonal selection principle is one of the models used to incorporate the behaviour of the AIS. The biological principles like clone generation, proliferation and maturation are mimicked and incorporated into this algorithm. In order to find and manage the pareto optimal set a non dominated sorting technique and crowding distance measure have been used. The proposed multi-objective ACSA (MOACSA) method has been tested on two different test systems and the results are compared with other methods reported in literature.
Keywords :
Pareto optimisation; artificial immune systems; economics; emission; load dispatching; power system management; AIS; EED problem; MOACSA; OPF problem; Pareto optimal; adaptive clonal selection algorithm; artificial immune system; crowding distance measure; economic emission dispatch; emission constraints; multiobjective ACSA; nondominated sorting technique; power demand equality; power system; Cloning; Fuels; Generators; Immune system; Linear programming; Pareto optimization; Adaptive clonal selection algorithm; economic emission dispatch; fuel cost; multi-objective optimization; optimal power flow; single objective optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
India Conference (INDICON), 2013 Annual IEEE
Conference_Location :
Mumbai
Print_ISBN :
978-1-4799-2274-1
Type :
conf
DOI :
10.1109/INDCON.2013.6725969
Filename :
6725969
Link To Document :
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