Multi-source heterogeneous data fusion of a distribution network based on a joint Kalman filter

被引：0

作者：

Xia W. ^{[1
]}

Cai W. ^{[1
]}

Liu Y. ^{[1
]}

Li H. ^{[2
]}

机构：

[1] China Southern Power Grid Digital Grid Research Institute Co., Ltd., Guangzhou

[2] Wuhan University, Wuhan

来源：

Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2022年 / 50卷 / 10期

关键词：

filling; fusion; joint Kalman filter; multi source heterogeneous data; registration;

D O I：

10.19783/j.cnki.pspc.211485

中图分类号：

学科分类号：

摘要：

The traditional method does not register the time of multi-source heterogeneous data collection in the distribution network. This results in large errors and low efficiency in the fusion of multi-source heterogeneous data. Thus a distribution network multi-source heterogeneous data fusion method based on joint Kalman filtering is proposed. First, a data correction mechanism is constructed, the least squares method is used to register the time of data collection, and the Lagrangian interpolation method is used to fill the time series data to calculate the data relevance. Then, the joint Kalman filtering algorithm is used to fuse the same data into the same class, so as to realize the multi-source heterogeneous data fusion of the distribution network. Experimental results show that the method not only can track the fusion error of the demand according to the demand, but also reduce the relative error of node voltage and power estimation, and improve the efficiency of multi-source heterogeneous data fusion, demonstrating the effectiveness of the proposed method and applicability of the joint Kalman filter algorithm in data fusion. © 2022 Power System Protection and Control Press. All rights reserved.

引用

页码：180 / 187

页数：7

共 29 条

[1]

LIN Jieyu, CUI Weiping, Power-data-asset trading platform architecture based on double-chained blockchain, Electric Power, 54, 11, pp. 164-170, (2021)

[2]

YIN Jijun, PAN Weiwei, Research and application of resumption power index based on power big data, Zhejiang Electric Power, 40, 2, pp. 26-32, (2021)

[3]

LI Qing, Power big data anomaly detection method based on an improved PSO-PFCM clustering algorithm, Power System Protection and Control, 49, 18, pp. 161-166, (2021)

[4]

YE Yuanbo, HUANG Taigui, XIE Min, Et al., A state assessment method for intelligent substation secondary equipment based on multi-model ensemble learning, Power System Protection and Control, 49, 12, pp. 148-152, (2021)

[5]

WANG Yuantao, PAN Wulue, MA Wei, Et al., State assessment of power secondary equipment based on data center and matter-element extension theory, Zhejiang Electric Power, 40, 12, pp. 111-116, (2021)

[6]

WANG Gang, LIU Jingwen, LI Guopeng, Et al., System protection of a pipe corridor power cabin based on multi-source heterogeneous data fusion, Power System Protection and Control, 49, 7, pp. 103-109, (2021)

[7]

YANG Kai, BIAN Yuekui, YANG Ningguo, Et al., Information fusion method of infrared multi-station passive joint tracking, Journal of Gun Launch & Control, 41, 3, pp. 49-52, (2020)

[8]

WANG Yi, WANG Zhiwei, HE Xin, Data middle platform construction and application of intelligent power stations, Electric Power, 54, 3, pp. 61-67, (2021)

[9]

ZHANG Qingchun, HE Xiaoci, YAO Sheng, Et al., Research on the fusion technology of camera and lidar based on ROS intelligent mobile robot, China Measurement & Test, 47, 12, pp. 120-123, (2021)

[10]

ZHANG Lei, LIU Xintong, CAI Shuo, Et al., Stream data processing of the power internet of things based on Storm architecture, Power System Protection and Control, 49, 20, pp. 112-119, (2021)

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