A Data-Driven Slow Dynamic Characteristic Extraction and State Estimation Method for Large Power Grid

The steady-state time-varying law of the grid state variables caused by load fluctuations is the slow dynamic characteristic of the grid, and the rapid extraction of its characteristics is the basis for the rapid estimation of the large scale grid operation state and the optimization of static stability prevention and control. To solve the computation speed problem of large power grid system state calculation by load prediction multiplied by jacobian inverse matrix, a data-driven slow dynamic characteristic extraction and state estimation method are proposed in this paper. Based on the various characteristics of the PV curve determined by bifurcation theory and continuous power flow, a series of time-varying second-order polynomials of bus voltage is used to represent the slow dynamic characteristics. The estimated operation state can be quickly calculated by short-term historical operating data according to this characteristic. The accuracy and rapidity of the proposed method are demonstrated on several test systems.