Research on Optimal Projection Integration Algorithm for Multi-Sources Heterogeneous Missing Data in Power Grid
The strong dispersion of multi-source heterogeneous data in power grids leads to a serious problem of random missing data,which affects the management quality of the overall operation of power grids.A power grid multi-source heterogeneous missing data optimal projection integration algorithm is studied.Firstly,the Lagrange difference method is introduced to fill the missing values in the multi-source heterogeneous data of power grid.Secondly,an empirical mode decomposition method is used to eliminate the noise in the grid multi-source heterogeneous data.Finally,the projection index function is established to transform the high-dimensional power grid multi-source heterogeneous data into one-dimensional projection values after projection processing.The iterative adaptation of fuzzy clustering is calculated,and utilize the chaotic cultural difference evolution algorithm to find the optimal projection direction of the power grid multi-source heterogeneous data to complete the integration of the missing data.The experimental results show that the proposed algorithm has a better data denoising effect,and there is no distortion of the power grid multi-source heterogeneous data after denoising,and it can ensure the stability of the algorithm operation while improving the integration accuracy.The data integration accuracy of the proposed algorithm is less affected by missing data.The research can improve the quality of the multi-source heterogeneous data of the power grid and help to improve the operation and management level of the power grid.
Power grid multi-source heterogeneous dataData fillingProjection index functionLagrange difference methodData integrationOptimal projection direction
NETL
NSTL
万方数据
维普
