首页> 外文期刊>Mathematical Problems in Engineering >Adaptive Differential Evolution Based on Simulated Annealing for Large-Scale Dynamic Economic Dispatch with Valve-Point Effects
【24h】

Adaptive Differential Evolution Based on Simulated Annealing for Large-Scale Dynamic Economic Dispatch with Valve-Point Effects

机译:基于模拟退火的带阀点效应的大型动态经济调度自适应差分进化

获取原文
获取原文并翻译 | 示例
           

摘要

Dynamic economic dispatch (DED) that considers valve-point effects is a complex nonconvex and nonsmooth optimization problem in power systems. Over the past few decades, multiple approaches have been developed to solve this problem. In this paper, an adaptive differential evolution based on simulated annealing algorithm is proposed to solve the DED problem with valvepoint effects. Simulated annealing (SA) algorithm is employed to carry out an adaptive selection mechanism in which the mutation operators of differential evolution (DE) are selected adaptively based on their historical performance. A mutation operator pool consisting of five operators is built to make each operator show its strength at different stages of the evolutionary process. Moreover, a heuristic strategy is introduced to transform infeasible solutions towards feasible ones to enhance the convergence rate of the proposed algorithm. The effectiveness of the proposed methods is demonstrated first on 10 popular benchmark functions with 100 dimensions, in comparison with the classic DE and five variants. Then, it is used to solve four DED problems with 10, 15, 30, and 54 units, which consider the valve-point effects, transmission loss, and prohibited operating zones. The simulation results are compared with those of state-of-the-art algorithms to clarify the significance of the proposed method and verify its performance. Three systems with 100-500 generators are also tested to confirm the advantages of the proposed method on large-scale DED problem.
机译:考虑阀点效应的动态经济调度(DED)是电力系统中复杂的非凸且非平滑的优化问题。在过去的几十年中,已经开发出多种方法来解决该问题。提出了一种基于模拟退火算法的自适应微分进化算法,以解决带阀点效应的DED问题。采用模拟退火(SA)算法来执行自适应选择机制,其中根据其历史表现来自适应地选择差分进化(DE)的变异算子。建立了一个由五个算子组成的变异算子池,以使每个算子在进化过程的不同阶段都能发挥其优势。此外,引入了启发式策略,将不可行的解决方案转换为可行的解决方案,以提高所提出算法的收敛速度。与经典DE和五个变体相比,该方法的有效性首先在100个维度的10个流行基准函数上得到了证明。然后,它用于解决10个,15个,30个和54个单元的四个DED问题,这些问题考虑了阀点效应,传输损失和禁止的操作区域。将仿真结果与最新算法进行了比较,以阐明所提出方法的重要性并验证其性能。还测试了三个具有100-500个发电机的系统,以确认该方法在大规模DED问题上的优势。

著录项

  • 来源
    《Mathematical Problems in Engineering》 |2018年第14期|4745192.1-4745192.16|共16页
  • 作者单位

    Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China|Northeastern Univ, State Key Lab Integrated Automat Proc Ind, Shenyang, Liaoning, Peoples R China;

    Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China;

    Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China;

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

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号