Connected Slice Identification Method Based on Incidence Matrix Compression and Branch Pointer Vector Update

Abstract

The invention discloses a connected piece identification method based on association matrix compression and branch pointer vector update, which solves the problem of how to efficiently and reliably perform network connected piece identification. The invention comprises the following steps: step 1, generating a branch-node association matrix of the network according to the network branch-node related information; step 2, searching the zero-column of the branch-node association matrix to identify an isolated node; step 3, "compressing" the branch-node association matrix by rows, leaving only thecolumn labels of non-zero elements to obtain a plurality of binary connected sets; step 4, performing column scan on the branch-node association matrix and updating the branch pointer vector; and step 5, merging and growing the binary connected set according to the final branch pointer vector to obtain the final connected slice set. The method avoids a large number of graph search and logic operations, with no need to perform matrix decomposition operation, and is efficient and reliable to be suitable for advantages of accelerating by using sparse technology, etc.

Description

technical field [0001] The invention relates to the technical field of power system simulation analysis, in particular to a connected slice identification method based on correlation matrix compression and branch pointer vector update. Background technique [0002] The identification of connected slices of the network is an important content of topology analysis and the basis of various power grid analysis and calculation application modules. Traditional topology analysis algorithms generally include graph theory search method, adjacency matrix method, incidence matrix method or their mixed algorithms. The graph theory search method is generally based on the linked list relationship, and realizes the analysis of node connectivity by tracing the node path, mainly including depth-first search and breadth-first search algorithms. This type of algorithm is easy to understand, but the search process will become slower when the node size is large, and even fall into infinite dept...

AI Technical Summary

Problems solved by technology

The correlation matrix method uses the branch-node correlation matrix or loop-node correlation matrix for topology analysis. Related literature proposes a topology analysis algorithm based on the LU decomposition of the branch-node correlation matrix. Through the upper triangular matrix after the LU decomposition of the correlation matrix U performs node reordering, adjacency identification and node merging calculation, which can realize the division of connected slices, but the matrix decomposition process and merging calculation are more cumbersome

For application scenarios that only need to identify node connectivity information in the network and do not need to give the path sequence, topology analysis algorithms such as search algorithms often have more redundant calculations.

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