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Perbandingan Metode Gaussian Particle Swarm Optimization (GPSO) dan Lagrange Multiplier pada Masalah Economic Dispatch

机译:高斯粒子群优化(GPSO)和拉格朗日乘数法在经济调度问题上的比较

摘要

On electric power system operation, economic planning problem is one variable to take into account due to operational cost efficiency. Economic Dispatch problem of electric power generation is discussed in this study to manage the output division on several units based on the the required load demand, with minimum operating cost yet is able to satisfy equality and inequality constraint of all units and system. In this study the Economic Dispatch problem which has non linear cost function is solved using swarm intelligent method is Gaussian Particle Swarm Optimization (GPSO) and Lagrange Multiplier. GPSO is a population-based stochastic algorithms which their moving is inspired by swarm intelligent and probabilities theories. To analize its accuracy, the Economic Dispatch solution by GPSO method is compared with Lagrange Multiplier method. From the test result it is proved that GPSO method gives economic planning calculation better than Lagrange Multiplier does.
机译:在电力系统运行中,由于运营成本效率,经济计划问题是要考虑的一个变量。本研究探讨了发电的经济调度问题,以基于所需的负荷需求来管理多个机组的输出分配,并以最低的运行成本满足了所有机组和系统的平等和不平等约束。在这项研究中,使用高斯粒子群优化(GPSO)和拉格朗日乘数法的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。 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GPSO方法的经济调度解决方案与Lagrange乘数方法进行了比较。从测试结果可以证明,GPSO方法比拉格朗日乘数法能更好地进行经济计划计算。

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    Komsiyah, Siti;

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  • 年度 2012
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