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
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