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

机译:改进的基于教学学习的多目标最优潮流问题优化

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摘要

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.
机译:本文分析了一种改进的基于教学的优化算法,以解决考虑总燃料成本和机组总排放的多目标最优潮流问题。优化算法的修改阶段利用了自适应小波变异策略。此外,提出了一种模糊聚类技术,除了用于下一次迭代的智能种群选择之外,还可以避免极大的存储库大小。这些技术使算法搜索更大的空间以找到最佳解,同时收敛速度仍然很好。 IEEE 30-Bus和57-Bus系统用于说明所提出算法的性能,并将结果与​​文献中的结果进行比较。验证了所提出的方法具有优于其他技术的性能。

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