The distribution networks sometimes suffer from excessive losses and voltage violations in densely populated areas. The aim of the present study is to improve the performance of a distribution network by successively ...The distribution networks sometimes suffer from excessive losses and voltage violations in densely populated areas. The aim of the present study is to improve the performance of a distribution network by successively applying mono-capacitor positioning, multiple positioning and reconfiguration processes using GA-based algorithms implemented in a Matlab environment. From the diagnostic study of this network, it was observed that a minimum voltage of 0.90 pu induces a voltage deviation of 5.26%, followed by active and reactive losses of 425.08 kW and 435.09 kVAR, respectively. Single placement with the NSGAII resulted in the placement of a 3000 kVAR capacitor at node 128, which proved to be the invariably neuralgic point. Multiple placements resulted in a 21.55% reduction in losses and a 0.74% regression in voltage profile performance. After topology optimization, the loss profile improved by 65.08% and the voltage profile improved by 1.05%. Genetic algorithms are efficient and effective tools for improving the performance of distribution networks, whose degradation is often dynamic due to the natural variability of loads.展开更多
A single-layer Artificial Neural Network (ANN) model was developed to predict the removal efficiency of Ni(II) ions from aqueous solution using shelled Moringa Oleifera seed (SMOS) powder. Batch experiments resulted i...A single-layer Artificial Neural Network (ANN) model was developed to predict the removal efficiency of Ni(II) ions from aqueous solution using shelled Moringa Oleifera seed (SMOS) powder. Batch experiments resulted into standardization of optimum conditions: biomass dosage (4.0 g), Ni(II) concentration (25 mg/L) volume (200 mL) at pH 6.5. A time of forty minutes was found sufficient to achieve the equilibrium. The ANN model was designed to predict sorption efficiency of SMOS for target metal ion by combining back propagation (BP) with principle component analysis. A sigmoid axon was used as transfer function for input and output layers. The Levenberg–Marquardt Algorithm (LMA) was applied, giving a minimum mean squared error (MSE) for training and cross validation at the ninth place of decimal.展开更多
相较于城市地区,农村配电网更为薄弱且冗余度低。近年来,随着农村分布式光伏并网数量增多以及新能源汽车充电需求上升,农村配电网建设刻不容缓。针对农村分布式光伏并网与电动汽车充放电共同作用所引发的配电网配置问题,综合考虑农村安...相较于城市地区,农村配电网更为薄弱且冗余度低。近年来,随着农村分布式光伏并网数量增多以及新能源汽车充电需求上升,农村配电网建设刻不容缓。针对农村分布式光伏并网与电动汽车充放电共同作用所引发的配电网配置问题,综合考虑农村安装光伏和充电桩的限制以及配电网自身约束条件,提出一种基于第二代非支配排序遗传算法(non-dominated sorting genetic algorithm II,NSGA-II)的农村配电网多目标优化配置方法,求解得出Pareto解集,并筛选出最优配置方案。通过与基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)对比,在某地农村的实地验证显示,该方法能够满足分布式光伏“应并尽并”和充电桩报装需求,同时符合农村配电网投资的经济性要求。展开更多
文摘The distribution networks sometimes suffer from excessive losses and voltage violations in densely populated areas. The aim of the present study is to improve the performance of a distribution network by successively applying mono-capacitor positioning, multiple positioning and reconfiguration processes using GA-based algorithms implemented in a Matlab environment. From the diagnostic study of this network, it was observed that a minimum voltage of 0.90 pu induces a voltage deviation of 5.26%, followed by active and reactive losses of 425.08 kW and 435.09 kVAR, respectively. Single placement with the NSGAII resulted in the placement of a 3000 kVAR capacitor at node 128, which proved to be the invariably neuralgic point. Multiple placements resulted in a 21.55% reduction in losses and a 0.74% regression in voltage profile performance. After topology optimization, the loss profile improved by 65.08% and the voltage profile improved by 1.05%. Genetic algorithms are efficient and effective tools for improving the performance of distribution networks, whose degradation is often dynamic due to the natural variability of loads.
文摘A single-layer Artificial Neural Network (ANN) model was developed to predict the removal efficiency of Ni(II) ions from aqueous solution using shelled Moringa Oleifera seed (SMOS) powder. Batch experiments resulted into standardization of optimum conditions: biomass dosage (4.0 g), Ni(II) concentration (25 mg/L) volume (200 mL) at pH 6.5. A time of forty minutes was found sufficient to achieve the equilibrium. The ANN model was designed to predict sorption efficiency of SMOS for target metal ion by combining back propagation (BP) with principle component analysis. A sigmoid axon was used as transfer function for input and output layers. The Levenberg–Marquardt Algorithm (LMA) was applied, giving a minimum mean squared error (MSE) for training and cross validation at the ninth place of decimal.
文摘相较于城市地区,农村配电网更为薄弱且冗余度低。近年来,随着农村分布式光伏并网数量增多以及新能源汽车充电需求上升,农村配电网建设刻不容缓。针对农村分布式光伏并网与电动汽车充放电共同作用所引发的配电网配置问题,综合考虑农村安装光伏和充电桩的限制以及配电网自身约束条件,提出一种基于第二代非支配排序遗传算法(non-dominated sorting genetic algorithm II,NSGA-II)的农村配电网多目标优化配置方法,求解得出Pareto解集,并筛选出最优配置方案。通过与基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)对比,在某地农村的实地验证显示,该方法能够满足分布式光伏“应并尽并”和充电桩报装需求,同时符合农村配电网投资的经济性要求。
