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Hybrid Swarm Algorithm for Multiobjective Optimal Power Flow Problem

机译：多目标最优潮流问题的混合群算法

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摘要

Optimal power flow problem plays a major role in the operation and planning of power systems. It assists in acquiring the optimized solution for the optimal power flow problem. It consists of several objective functions and constraints. This paper solves the multiobjective optimal power flow problem using a new hybrid technique by combining the particle swarm optimization and ant colony optimization. This hybrid method overcomes the drawback in local search such as stagnation and premature convergence and also enhances the global search with chemical communication signal. The best results are extracted using fuzzy approach from the hybrid algorithm solution. These methods have been examined with the power flow objectives such as cost, loss and voltage stability index by individuals and multiobjective functions. The proposed algorithms applied to IEEE 30 and IEEE 118-bus test system and the results are analyzed and validated. The proposed algorithm results record the best compromised solution with minimum execution time compared with the particle swarm optimization.
机译：最优潮流问题在电力系统的运行和规划中起着重要作用。它有助于获取针对最佳潮流问题的优化解决方案。它由几个目标功能和约束组成。本文结合粒子群算法和蚁群算法，采用一种新的混合技​​术解决了多目标最优潮流问题。这种混合方法克服了局部搜索中的诸如停滞和过早收敛的缺点，并且还增强了化学通信信号的全局搜索。使用模糊方法从混合算法解决方案中提取最佳结果。这些方法已经通过个体和多目标函数以诸如成本，损耗和电压稳定性指数等潮流目标进行了检验。该算法适用于IEEE 30和IEEE 118总线测试系统，并对结果进行了分析和验证。与粒子群算法相比，所提出的算法结果记录了执行时间最短的最佳折衷解决方案。

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  • 来源
    《Circuits and systems》 |2016年第11期|3589-3603|共15页
  • 作者

    K. Rajalashmi; S. U. Prabha;

  • 作者单位

    Department of Electrical and Electronics Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India;

    Department of Electrical and Electronics Engineering, Sri Ramakrishna Engineering College, Coimbatore, India;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Multiobjective; OPF; Optimization; Hybrid;

    机译：多目标;OPF;优化;杂种;

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