Power system state estimation method and system based on information matrix sparse solution

Abstract

The invention discloses a power system state estimation method and system. The method comprises the following steps: acquiring telemetering and telesignaling data of primary equipment of a power system in real time; constructing a power system node branch model; determining non-zero element distribution of the active jacobian matrix according to the node branch model, and determining non-zero element distribution of the active information matrix; performing node optimization numbering based on active information matrix non-zero element distribution; calculating an active Jacobian matrix and areactive Jacobian matrix according to the new node sequence to obtain a new active information matrix and a reactive information matrix, and performing factorization by adopting a Gaussian eliminationmethod to obtain an active information matrix factor table and a reactive information matrix factor table; and performing power system state estimation based on the active information matrix factor table and the reactive information matrix factor table. When the method is used for solving the information matrix and estimating the state of the power system, the memory operation in the solving process can be reduced, the calculation load is reduced, and the calculation efficiency of power grid state estimation is improved.

Description

Technical field [0001] The invention relates to the technical field of power system dispatch automation, in particular to a power system state estimation method and system for optimizing the solution process of a state estimation information matrix. Background technique [0002] At present, most of the state estimation procedures of the actual production operation system in the scheduling control system are based on the principle of least squares Fast Decoupled State Estimation (FDSE), and its iterative calculation formula is shown in the following formula. [0003] [0004] Where: z is the measurement vector (m-dimensional), Is the state quantity (n-dimensional), R is the measurement error variance matrix, h is the nonlinear measurement vector function, Is the Jacobian matrix, G=H T R -1 H is the information matrix, z generally includes measurements such as branch power, node injection power, and node voltage amplitude. It is the state quantity correction quantity. [0005] Calc...

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Problems solved by technology

Similar to the SPMM problem, the main problem of this implementation method is that when the sparse vector is expanded into a dense vector, zero elements that do not need to be stored are stored in the dense vector. When the number of computing nodes n is large, even if the program can use Spatial locality improves the hit rate, but a large amount of data still needs to be transferred between the cache and the main memory, which affects the efficiency of triangular decomposition

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