Extraction of Influencing Variables for Power System Static Stability Indicators Based on Reciprocal Information Entropy Causal Inference

With the increasing uncertain factors in the context of energy transition, power system is faced with challenges in terms of voltage, power angle, and other stability issues. It is urgently required to reveal the key variables that affect system unstable modes so as to enhance the level of risk prevention and control for the power grid. With the continuous improvement of data collection technology in power systems, data-driven analysis methods are playing an increasingly important role in power system operation analysis. The reciprocal information entropy causal inference (RIECI) method is applied to analyze the static stability indexes. The method can effectively illustrate the causal direction between relevant variables and accurately reflect the causal strength. Simulation examples have demonstrated the effectiveness of the RIECI method in identifying the main factors influencing different unstable modes of the system.