• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>EPJ Web of Conferences >Computational methods of Gaussian Particle Swarm Optimization (GPSO) and Lagrange Multiplier on economic dispatch issues (case study on electrical system of Java-Bali IV area)
【24h】

Computational methods of Gaussian Particle Swarm Optimization (GPSO) and Lagrange Multiplier on economic dispatch issues (case study on electrical system of Java-Bali IV area)

机译：经济调度问题上的高斯粒子群优化（GPSO）和拉格朗日乘数的计算方法（以Java-Bali IV地区的电气系统为例）

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The objective in this paper is about economic dispatch problem of electric power generation where scheduling the committed generating units outputs so as to meet the required load demand at minimum operating cost, while satisfying all units and system equality and inequality constraint. In the operating of electric power system, an economic planning problem is one of variables that its must be considered since economically planning will give more efficiency in operational cost. In this paper the economic dispatch problem which has non linear cost function solved by using swarm intelligent method is Gaussian Particle Swarm Optimization (GPSO) and Lagrange Multiplier. GPSO is a population-based stochastic algorithms which their moving inspired by swarm intelligent and probabilities theories. To analize its accuracy, the economic dispatch solution by GPSO method will be compared with Lagrange multiplier method. From the running test result the GPSO method give economically planning calculation which it better than Lagrange multiplier method and the GPSO method faster to getting error convergence. Therefore the GPSO method have better performance to getting global best solution than the Lagrange method.
机译：本文的目的是关于发电的经济调度问题，其中调度承诺的发电机组输出，以便以最小的运行成本满足所需的负载需求，同时满足所有机组以及系统的平等和不平等约束。在电力系统的运行中，经济计划问题是必须考虑的变量之一，因为经济计划会提高运行成本的效率。本文采用群体智能方法解决了具有非线性成本函数的经济调度问题，即高斯粒子群优化算法和拉格朗日乘数法。 GPSO是一种基于人口的随机算法，其移动受到群体智能和概率理论的启发。为了分析其准确性，将使用GPSO方法的经济调度解决方案与Lagrange乘数方法进行比较。从运行测试结果来看，GPSO方法可以经济地进行规划计算，它比Lagrange乘数法和GPSO方法要快，可以更快地实现误差收敛。因此，与Lagrange方法相比，GPSO方法在获得全局最佳解决方案方面具有更好的性能。

著录项

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