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A GA-Based Method for Computing MOF-Weighting Coefficients Along with Optimal Location and Sizing of DG Sources in Distribution Systems

机译:一种基于GA的用于计算MOF加权系数的方法以及分配系统中DG源的最佳位置和大小

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Minimization of a weighted-sum multi-objective-objective function (MOF) is aimed at obtaining the optimal locations and sizes of multi DG sources in distribution networks. The indices of the MOF are related to the characteristics of the distribution network including active power loss, deviation of bus voltage from nominal value and reactive power loss. In the literature, the weighting coefficients were assumed arbitrarily or determined according to the preferences of the decision maker. To the authors’ knowledge, this paper presents -for the first time- a Genetic Algorithm (GA) based method for computing the weighting coefficients while minimizing the MOF of the distribution network. The GA optimization is applied for the MOF after being penalty-formulated to avoid violation of distribution-network constraints. The index priority of the MOF is always given to the deviation of bus voltage from nominal value in conformity to power quality features as no sag or swell of bus voltage is allowed. The computed optimal locations and sizes of the DG sources agreed reasonably with those obtained using PSO, the particle swarm optimization, for a well-recognized distribution network.
机译:最小化加权的多目标 - 目标函数(MOF)旨在获得分发网络中的多DG源的最佳位置和大小。 MOF的指标与包括有源功率损耗的分配网络的特性有关,总线电压与标称值和无功损耗的偏差。在文献中,根据决策者的偏好任意或确定加权系数。为了作者的知识,本文介绍了第一次基于遗传算法(GA)用于计算加权系数的方法,同时最小化分发网络的MOF。在罚款制定后避免违反分配网络约束后,将为MOF应用GA优化。 MOF的指数优先级始终赋予母线电压与标称值符合电力质量特征的偏差,因为允许的总线电压的凹凸或膨胀。计算的最佳位置和DG源的尺寸合理地与使用PSO获得的那些,粒子群优化用于良好识别的分配网络。

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