Data-Sequence Modeling Based Causal Evaluation Method for Power Systems and Spatiotemporal Causality Variation Patterns

Abstract

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.