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首页> 外文期刊>International journal of Power and energy conversion >Principal component analysis-based real coded genetic algorithm for optimal reactive power dispatch
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Principal component analysis-based real coded genetic algorithm for optimal reactive power dispatch

机译:基于主成分分析的实数编码遗传算法优化无功分配

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摘要

This paper presents a new solution method for optimal reactive power dispatch (ORPD) problem based on principal component analysis (PCA). In heavily loaded power system, security, reliability and economy are some unavoidable concerns. Therefore, ORPD becomes an essential tool to achieve these goals. It is a highly non-linear constrained optimisation problem. A PCA-based real coded genetic algorithm (PCA-RCGA) is proposed in this paper to deal with such type of problem. In PCA-RCGA, PCA theory is applied in mutation operator to enhance convergence of conventional genetic algorithm by guiding the direction of stochastic search to reach near the global optimal solution effectively. The proposed PCA-RCGA is tested on standard IEEE-30 bus and IEEE-118 bus system for ORPD problem. The simulation results for both cases are compared with various optimisation techniques applied to ORPD available in literature.
机译:本文提出了一种基于主成分分析(PCA)的最优无功调度(ORPD)问题的新解决方法。在重负荷的电力系统中,安全性,可靠性和经济性是一些不可避免的问题。因此,ORPD成为实现这些目标的必要工具。这是一个高度非线性的约束优化问题。针对此类问题,本文提出了一种基于PCA的实数编码遗传算法(PCA-RCGA)。在PCA-RCGA中,PCA理论被应用于变异算子中,通过指导随机搜索的方向有效地达到全局最优解,从而增强了传统遗传算法的收敛性。提出的PCA-RCGA已在标准IEEE-30总线和IEEE-118总线系统上针对ORPD问题进行了测试。将两种情况的仿真结果与文献中适用于ORPD的各种优化技术进行了比较。

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