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

Abstract

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.