Hybrid multilayer preprocessing method and system for ill-conditioned power flow system

By adjusting the row and column scaling factors of the Jacobian matrix, combining approximate minimum sorting and incomplete LU decomposition, sparse L and U matrices are generated, which solves the problems of large memory consumption and poor convergence in large power systems, and improves stability and efficiency.

CN120474020AActive Publication Date: 2025-08-12WUXI RES INST OF APPLIED TECH TSINGHUA UNIV +1
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Patent Information

Application Number
CN202510555758.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-29
Publication Date
2025-08-12
Estimated Expiration
2045-04-29

AI Technical Summary

Technical Problem

The prior art has problems such as excessive memory consumption and poor scalability when dealing with large power systems, and conventional iterative methods are prone to diverge under severe pathological conditions, resulting in a decrease in numerical stability and hindered convergence.

Method used

A mixed multi-layer preprocessing method with 1-order norm scaling-AMD rearrangement-ILU incomplete decomposition is adopted. By adjusting the row and column scaling factors of the Jacobian matrix, the matrix element position is adjusted, combined with approximate minimum sorting and incomplete LU decomposition, sparse L and U matrices are generated, and a hybrid preconditioner is integrated.

Benefits of technology

It improves the stability and decomposition efficiency of the current calculation of the power system, reduces the risk of convergence failure, and balances the calculation accuracy and efficiency.

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Abstract

The invention discloses a hybrid multi-layer preprocessing method and system for an ill-conditioned power flow system, and mainly relates to the technical field of data calculation of a power system. Comprising the following steps: obtaining an original Jacobian matrix J based on a power flow calculation method of a power system, and executing l1 norm scaling on the original Jacobian matrix to obtain a scaled matrix; performing approximate minimum sorting on the scaling matrix to obtain a rearranged matrix; performing incomplete LU decomposition on the rearrangement matrix to obtain a decomposition matrix; and performing hybrid precondition device integration according to the scaling matrix, the rearrangement matrix and the decomposition matrix. The method has the beneficial effects that the risk of convergence failure is reduced, the decomposition efficiency is improved, and meanwhile, the precision and the calculation efficiency are balanced.
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