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首页> 外文期刊>International Journal of Engineering and Technology >Hybrid Real coded Genetic Algorithm - Differential Evolution for Optimal Power Flow
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Hybrid Real coded Genetic Algorithm - Differential Evolution for Optimal Power Flow

机译:混合实数编码遗传算法-最优功率流的差分进化

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Electric power providing companies has to deliver reliable electric power in quality and at minimum cost for the competition in deregulated environment. To know the power system security and operating state, power flows in lines and bus voltage magnitude and angle are calculated using power flow analysis, and to tune the power system for minimum operating cost while ensure security and reliability, Optimal Power Flow (OPF) is used. OPF is a non-convex, complex problem, conventional mathematical techniques gets struck in local optimal point based on the initial point. In recent years, heuristic algorithms are used to overcome local optimal solution and to reach global optimal solution. Heuristic algorithm Genetic Algorithm (GA) and Real coded GA (RGA) has better Selection and Cross-over operation, Differential Evolution (DE) has better Mutation process, inspires the hybrid of RGA ? DE algorithm, presented in this paper. In this work RGA, DE and hybrid RGA ? DE algorithms are used to find OPF solutions for a standard test IEEE 30 bus system.
机译:电力供应公司必须在管制放松的环境中以高质量和最低成本交付可靠的电力。要了解电力系统的安全性和运行状态,可使用电力流分析来计算线路中的电力流以及母线电压幅度和角度,并在确保安全性和可靠性的同时调整电力系统的最低运行成本,用过的。 OPF是一个非凸,复杂的问题,传统的数学技术在基于初始点的局部最优点中受到冲击。近年来,启发式算法用于克服局部最优解并达到全局最优解。启发式算法遗传算法(GA)和实编码GA(RGA)具有更好的选择和交叉操作,差分进化(DE)具有更好的变异过程,启发了RGA的混合动力。本文提出了DE算法。在这项工作中,RGA,DE和混合RGA? DE算法用于为标准测试IEEE 30总线系统找到OPF解决方案。

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