A novel spatial interpolation method based on integrated radial basis function artificial neural networks （IRBFANNs） is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at u...A novel spatial interpolation method based on integrated radial basis function artificial neural networks （IRBFANNs） is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at un- sampled sites in a mountain region. The IRBFANNs hybridize the advantages of the artificial neural networks and the neural networks integration approach. Three experimental projects under different sampling densities are carried out to study the performance of the proposed IRBFANNs-based interpolation method. This novel method is compared with six peer spatial interpolation methods based on the root mean square error and visual evaluation of the distribution maps of Mn elements. The experimental results show that the proposed method performs better in accuracy and stability. Moreover, the proposed method can provide more details in the spatial distribution maps than the compared interpolation methods in the cases of sparse sampling density.展开更多
为了精确识别直流配电网故障线路,缩小失电范围,并降低支节点附近故障选线盲区,提出了基于变相位系数–电磁时间反转(variable phase coefficient-electromagnetic time reversal,VPC-EMTR)的多端故障选线方法。该方法根据配电网拓扑和...为了精确识别直流配电网故障线路,缩小失电范围,并降低支节点附近故障选线盲区,提出了基于变相位系数–电磁时间反转(variable phase coefficient-electromagnetic time reversal,VPC-EMTR)的多端故障选线方法。该方法根据配电网拓扑和线路参数建立了无损镜像线路网络,利用测量点处的时间反转后的1模电流在无损镜像网络中建立电流源,并计算该线路网络中每一点处的假想故障的故障电流有效值,最大有效值所处线路即为故障线路。该方法设置各镜像支路的相位系数与其长度呈高斯分布函数关系,使得支节点附近的故障测距结果偏移至线路中间处。同时,该方法利用最少测量点二次计算故障选线结果,减少了多余测量点对选线结果的影响,保证了故障选线结果的可靠性。在理论上对该方法进行了证明,并在PSCAD中建立了“手拉手”型多端直流配电网络来验证该方法的有效性,仿真结果表明:基于VPC-EMTR的多端故障选线法的选线结果准确,能够减少支节点附近选线的盲区。展开更多
基金The National Natural Science Foundation of China(No.61261007,61062005)the Key Program of Yunnan Natural Science Foundation(No.2013FA008)
文摘A novel spatial interpolation method based on integrated radial basis function artificial neural networks （IRBFANNs） is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at un- sampled sites in a mountain region. The IRBFANNs hybridize the advantages of the artificial neural networks and the neural networks integration approach. Three experimental projects under different sampling densities are carried out to study the performance of the proposed IRBFANNs-based interpolation method. This novel method is compared with six peer spatial interpolation methods based on the root mean square error and visual evaluation of the distribution maps of Mn elements. The experimental results show that the proposed method performs better in accuracy and stability. Moreover, the proposed method can provide more details in the spatial distribution maps than the compared interpolation methods in the cases of sparse sampling density.
文摘为了精确识别直流配电网故障线路,缩小失电范围,并降低支节点附近故障选线盲区,提出了基于变相位系数–电磁时间反转(variable phase coefficient-electromagnetic time reversal,VPC-EMTR)的多端故障选线方法。该方法根据配电网拓扑和线路参数建立了无损镜像线路网络,利用测量点处的时间反转后的1模电流在无损镜像网络中建立电流源,并计算该线路网络中每一点处的假想故障的故障电流有效值,最大有效值所处线路即为故障线路。该方法设置各镜像支路的相位系数与其长度呈高斯分布函数关系,使得支节点附近的故障测距结果偏移至线路中间处。同时,该方法利用最少测量点二次计算故障选线结果,减少了多余测量点对选线结果的影响,保证了故障选线结果的可靠性。在理论上对该方法进行了证明,并在PSCAD中建立了“手拉手”型多端直流配电网络来验证该方法的有效性,仿真结果表明:基于VPC-EMTR的多端故障选线法的选线结果准确,能够减少支节点附近选线的盲区。
