A modified teaching–learning based optimization for multi-objective optimal power flow problem

•A new modified teaching–learning based algorithm is proposed.•A self-adaptive wavelet mutation strategy is used to enhance the performance.•To avoid reaching a large repository size, a fuzzy clustering technique is used.•An efficiently smart population selection is utilized.•Simulations show the su...

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Bibliographic Details
Published inEnergy conversion and management Vol. 77; pp. 597 - 607
Main Authors Shabanpour-Haghighi, Amin, Seifi, Ali Reza, Niknam, Taher
Format Journal Article
LanguageEnglish
Published Kidlington Elsevier Ltd 01.01.2014
Elsevier
Subjects
Algorithms
Applied sciences
Cost analysis
Emission analysis
Energy
Exact sciences and technology
Fuzzy
Modified teaching–learning based optimization
Multi-objective problem
Optimal power flow
Optimization
Pareto-optimal set
Power flow
Repositories
Searching
Optimal power flow
Pareto-optimal set
Modified teaching–learning based optimization
Multi-objective problem
Modified teaching-learning based optimization
Optimization
Teaching
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Summary:•A new modified teaching–learning based algorithm is proposed.•A self-adaptive wavelet mutation strategy is used to enhance the performance.•To avoid reaching a large repository size, a fuzzy clustering technique is used.•An efficiently smart population selection is utilized.•Simulations show the superiority of this algorithm compared with other ones. In this paper, a modified teaching–learning based optimization algorithm is analyzed to solve the multi-objective optimal power flow problem considering the total fuel cost and total emission of the units. The modified phase of the optimization algorithm utilizes a self-adapting wavelet mutation strategy. Moreover, a fuzzy clustering technique is proposed to avoid extremely large repository size besides a smart population selection for the next iteration. These techniques make the algorithm searching a larger space to find the optimal solutions while speed of the convergence remains good. The IEEE 30-Bus and 57-Bus systems are used to illustrate performance of the proposed algorithm and results are compared with those in literatures. It is verified that the proposed approach has better performance over other techniques.
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ISSN:0196-8904
1879-2227
DOI:10.1016/j.enconman.2013.09.028