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Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization
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DOI：10.1016/j.ijepes.2013.04.011.png)
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摘要
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This paper presents a newly developed teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal reactive power dispatch (ORPD) problem by minimizing real power loss, voltage deviation and voltage stability index. To accelerate the convergence speed and to improve solution quality quasi-opposition based learning (QOBL) concept is incorporated in original TLBO algorithm. The proposed TLBO and quasi-oppositional TLBO (QOTLBO) approaches are implemented on standard IEEE 30-bus and IEEE 118-bus test systems. Results demonstrate superiority in terms of solution quality of the proposed QOTLBO approach over original TLBO and other optimization techniques and confirm its potential to solve the ORPD problem. (C) 2013 Elsevier Ltd. All rights reserved.
Keyword:
Optimal reactive power dispatch
Teaching learning based optimization
Opposition based learning
Evolutionary algorithm
Voltage stability index
Voltage profile
期刊
I
IF:
5
论文数:
1.1W
被引数:
3.1W
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