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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2013 Vol.3(6): 529-533 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.375

Artificial Immune Algorithm for Practical Power Economic Dispatch Problems

C. L. Chiang
Abstract—This paper proposes an artificial immune algorithm with a multiplier updating method (AIA-MU) for practical power economic dispatch (PED) considering units with prohibited operating zones (POZ). The AIA equipped with a migration operation can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to handle the system constraints. To show the advantages of the proposed algorithm, two examples are investigated, and the computational results of the proposed method are compared with that of the previous methods. The proposed approach integrates the AIA and the MU, revealing that the proposed approach has the following merits - ease of implementation; applicability to non-convex fuel cost functions of the POZ; better effectiveness than previous methods, and the requirement for only a small population in applying the optimal PED problem of generators with POZ.

Index Terms—Power economic dispatch, artificial immune algorithm, prohibited operating zones.

C. L. Chiang is with the Department of Electronic Engineering, Nan Kai University of Technology, Taiwan, ROC (e-mail: t129r@ nkut.edu.tw).


Cite:C. L. Chiang, "Artificial Immune Algorithm for Practical Power Economic Dispatch Problems," International Journal of Machine Learning and Computing vol.3, no. 6, pp. 529-533, 2013.

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