基于系统环境数据统计特性的戴维南等效参数辨识

This paper proposes a novel method for identifying Thévenin equivalent parameters (TEP) in power system, based on the statistical characteristics of the system's stochastic response. The method leverages stochastic fluctuation data under steady-state grid conditions and applies sliding window techniques to compute sensitivity parameters between voltage magnitude, current magnitude and power. This enables high-accuracy and robust TEP identification. In contrast to traditional methods, the proposed approach does not rely on large disturbances or probing signals but instead utilizes the natural fluctuation behavior of the system. Additionally, the method supports distributed implementation using local measurements of voltage magnitude, current magnitude, and power, offering significant practical value for engineering applications. The theoretical analysis demonstrates the method's robustness in the presence of low signal-to-noise ratio (SNR), asynchronous measurements, and data collinearity issues. Simulation results further confirm the effectiveness of the proposed method in diverse practical scenarios, demonstrating its ability to consistently provide accurate and reliable identification of TEP using system ambient data.