The data acquisition technologies used in power systems have been continuously improving

thus laying the solid foundation for data-driven operation analysis of power systems. However

existing methods for analyzing the relationship between operational variables mainly depend on the mathematical model and element parameters of the power system. Therefore

a thorough data-based analysis method is required to investigate the spatiotemporal characteristics of power system operation

especially for new types of power systems. The causal inference method

which has been successfully applied in many fields

is a powerful tool for investigating the interaction of data variables. In this study

a causal inference method is proposed based on supervisory control and data acquisition (SCADA) data for investigating the spatiotemporal causal relationships in power systems. Initially

a multiple data-sequence regression model is proposed to analyze the relationship of operation data variables. Next

the linear non-Gaussian acyclic model (LiNGAM) is used to calculate the causal index of the operational variables

and its limitations are analyzed. Furthermore

a new causal index of “full variable amplitude LiNGAM (FVA-LiNGAM)” is proposed by incorporating prior causal direct knowledge and considering the effect of real variable amplitude. Using the FVA-LiNGAM causal index

the causal relationship of operation variables can be investigated with higher spatiotemporal accuracy than that of the original LiNGAM index. Taking a real SCADA data subset of a provincial power system as an example

the validity of the FVA-LiNGAM causal index is verified. The variation patterns in spatiotemporal causality are explored using actual SCADA data sequences. The result shows that there indeed exists some spatiotemporal causality variation patterns between the operating variables of the power system.