In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the d...In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the distribution network.To effectively improve the lean management of LVSA,the paper proposes an identification method for the UTR based on Local Selective Combination in ParallelOutlier Ensembles algorithm(LSCP).Firstly,the voltage data is reconstructed based on the information entropy to highlight the differences in between.Then,the LSCP algorithmcombines four base outlier detection algorithms,namely Isolation Forest(I-Forest),One-Class Support VectorMachine(OC-SVM),Copula-Based Outlier Detection(COPOD)and Local Outlier Factor(LOF),to construct the identification model of UTR.This model can accurately detect users’differences in voltage data,and identify users with wrong UTR.Meanwhile,the key input parameter of the LSCP algorithm is determined automatically through the line loss rate,and the influence of artificial settings on recognition accuracy can be reduced.Finally,thismethod is verified in the actual LVSA where the recall and precision rates are 100%compared with othermethods.Furthermore,the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed.The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually.And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity,which improves the stability and accuracy of UTR identification in LVSA.展开更多
Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of ...Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.展开更多
目的研究交趾黄檀Dalbergia cochinchinensis Pierre ex Laness的新黄酮类成分及其抗H9c2心肌细胞缺氧/复氧损伤活性。方法交趾黄檀70%乙醇提取物采用硅胶、Sephadex LH-20、反相制备HPLC进行分离纯化,根据理化性质及波谱数据鉴定所得...目的研究交趾黄檀Dalbergia cochinchinensis Pierre ex Laness的新黄酮类成分及其抗H9c2心肌细胞缺氧/复氧损伤活性。方法交趾黄檀70%乙醇提取物采用硅胶、Sephadex LH-20、反相制备HPLC进行分离纯化,根据理化性质及波谱数据鉴定所得化合物的结构。采用CCK-8法检测其对H9c2心肌细胞的活性及对H9c2细胞缺氧/复氧损伤的保护作用,并分析其构效关系。结果从中分离得到12个化合物,分别鉴定为阔叶黄檀酚(1)、5-O-methyllatifolin(2)、mimosifoliol(3)、5-O-methydalbergiphenol(4)、dalbergiphenol(5)、cearoin(6)、2,4-dihydroxy-5-methoxy-benzophenone(7)、2-hydroxy-4,5-dimethoxybenzophenone(8)、melannoin(9)、2,2′,5-trihydroxy-4-methoxybenzophenone(10)、黄檀素(11)、4-甲氧基黄檀醌(12)。黄檀酚及黄檀内酯类化合物对H9c2细胞毒性较小,黄檀酚类化合物抗H9c2心肌细胞缺氧/复氧损伤活性较强。结论化合物8为新天然产物,化合物4、9为首次从该植物中分离得到。黄檀酚类化合物可能是抗H9c2细胞缺氧/复氧损伤的主要新黄酮类成分。展开更多
低压台区拓扑信息的准确记录是进行台区线损分析、三相不平衡治理等工作的基础。针对目前拓扑档案排查成本高且效率低的问题,提出一种基于自适应k近邻(adaptive k nearest neighbor,AKNN)异常检验和自适应密度峰值(adaptive density pea...低压台区拓扑信息的准确记录是进行台区线损分析、三相不平衡治理等工作的基础。针对目前拓扑档案排查成本高且效率低的问题,提出一种基于自适应k近邻(adaptive k nearest neighbor,AKNN)异常检验和自适应密度峰值(adaptive density peaks clustering,ADPC)聚类的低压台区拓扑识别方法。该方法利用动态时间弯曲(dynamic time warping,DTW)距离度量低压台区用户间电压序列的相似性,通过AKNN异常检验算法检验并校正异常的用户与变压器之间的关系(简称“户变关系”),在得到正确户变关系的基础上,采用ADPC聚类算法对台区内用户进行相位识别;最后,通过实际台区算例分析验证了该方法不需要人为设置参数,能有效实现低压台区的拓扑识别,具有较高的适用性与准确性。展开更多
文摘In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the distribution network.To effectively improve the lean management of LVSA,the paper proposes an identification method for the UTR based on Local Selective Combination in ParallelOutlier Ensembles algorithm(LSCP).Firstly,the voltage data is reconstructed based on the information entropy to highlight the differences in between.Then,the LSCP algorithmcombines four base outlier detection algorithms,namely Isolation Forest(I-Forest),One-Class Support VectorMachine(OC-SVM),Copula-Based Outlier Detection(COPOD)and Local Outlier Factor(LOF),to construct the identification model of UTR.This model can accurately detect users’differences in voltage data,and identify users with wrong UTR.Meanwhile,the key input parameter of the LSCP algorithm is determined automatically through the line loss rate,and the influence of artificial settings on recognition accuracy can be reduced.Finally,thismethod is verified in the actual LVSA where the recall and precision rates are 100%compared with othermethods.Furthermore,the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed.The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually.And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity,which improves the stability and accuracy of UTR identification in LVSA.
基金Under the auspices of Natural Science Foundation of China(No.41971166)。
文摘Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.
文摘目的研究交趾黄檀Dalbergia cochinchinensis Pierre ex Laness的新黄酮类成分及其抗H9c2心肌细胞缺氧/复氧损伤活性。方法交趾黄檀70%乙醇提取物采用硅胶、Sephadex LH-20、反相制备HPLC进行分离纯化,根据理化性质及波谱数据鉴定所得化合物的结构。采用CCK-8法检测其对H9c2心肌细胞的活性及对H9c2细胞缺氧/复氧损伤的保护作用,并分析其构效关系。结果从中分离得到12个化合物,分别鉴定为阔叶黄檀酚(1)、5-O-methyllatifolin(2)、mimosifoliol(3)、5-O-methydalbergiphenol(4)、dalbergiphenol(5)、cearoin(6)、2,4-dihydroxy-5-methoxy-benzophenone(7)、2-hydroxy-4,5-dimethoxybenzophenone(8)、melannoin(9)、2,2′,5-trihydroxy-4-methoxybenzophenone(10)、黄檀素(11)、4-甲氧基黄檀醌(12)。黄檀酚及黄檀内酯类化合物对H9c2细胞毒性较小,黄檀酚类化合物抗H9c2心肌细胞缺氧/复氧损伤活性较强。结论化合物8为新天然产物,化合物4、9为首次从该植物中分离得到。黄檀酚类化合物可能是抗H9c2细胞缺氧/复氧损伤的主要新黄酮类成分。
文摘低压台区拓扑信息的准确记录是进行台区线损分析、三相不平衡治理等工作的基础。针对目前拓扑档案排查成本高且效率低的问题,提出一种基于自适应k近邻(adaptive k nearest neighbor,AKNN)异常检验和自适应密度峰值(adaptive density peaks clustering,ADPC)聚类的低压台区拓扑识别方法。该方法利用动态时间弯曲(dynamic time warping,DTW)距离度量低压台区用户间电压序列的相似性,通过AKNN异常检验算法检验并校正异常的用户与变压器之间的关系(简称“户变关系”),在得到正确户变关系的基础上,采用ADPC聚类算法对台区内用户进行相位识别;最后,通过实际台区算例分析验证了该方法不需要人为设置参数,能有效实现低压台区的拓扑识别,具有较高的适用性与准确性。
