Data-driven Estimation of the Power Flow Jacobian Matrix in High Dimensional Space
For power system operators, it offers data-driven Jacobian estimation that adapts to topology changes, though the methods are incremental refinements of existing techniques.
This paper proposes data-driven methods, including least-squares and neural network-based approaches, to estimate the power flow Jacobian matrix in high-dimensional space, validated against benchmark values.
The Jacobian matrix is the core part of power flow analysis, which is the basis for power system planning and operations. This paper estimates the Jacobian matrix in high dimensional space. Firstly, theoretical analysis and model-based calculation of the Jacobian matrix are introduced to obtain the benchmark value. Then, the estimation algorithms based on least-squared errors and the deviation estimation based on the neural network are studied in detail, including the theories, equations, derivations, codes, advantages and disadvantages, and application scenes. The proposed algorithms are data-driven and sensitive to up-to-date topology parameters and state variables. The efforts are validate by comparing the results to benchmark values.