an improved artificial bee colony algorithm and its application to multi-objective optimal power flow

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ID: 177299
2015
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Abstract
Optimal power flow (OPF) objective functions involve minimization of the total fuel costs of generating units, minimization of atmospheric pollutant emissions, minimization of active power losses and minimization of voltage deviations. In this paper, a fuzzy multi-objective OPF model is established by the fuzzy membership functions and the fuzzy satisfaction-maximizing method. The improved artificial bee colony (IABC) algorithm is applied to solve the model. In the IABC algorithm, the mutation and crossover operations of a differential evolution algorithm are utilized to generate new solutions to improve exploitation capacity; tent chaos mapping is utilized to generate initial swarms, reference mutation solutions and the reference dimensions of crossover operations to improve swarm diversity. The proposed method is applied to multi-objective OPF problems in IEEE 30-bus, IEEE 57-bus and IEEE 300-bus test systems. The results are compared with those obtained by other algorithms, which demonstrates the effectiveness and superiority of the IABC algorithm, and how the optimal scheme obtained by the proposed model can make systems more economical and stable.
Reference Key
he2015energiesan Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Xuanhu He;Wei Wang;Jiuchun Jiang;Lijie Xu
Journal acs combinatorial science
Year 2015
DOI
10.3390/en8042412
URL
http://www.mdpi.com/1996-1073/8/4/2412
Keywords
artificial bee colony algorithm

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