Fast-data-reading-based application of sparse symmetric factor table method to polar-coordinate (PQ) decomposition method flow

Abstract

The invention discloses a fast-data-reading-based application of a sparse symmetric factor table method to polar-coordinate (PQ) decomposition method flow.A factor table is fast formed with the sparse symmetric matrix technology; list corner marks of triangle nonzero elements on the factor table and the number of the list corner marks are recorded; the sparse symmetric matrix technology is used for following fast front substitution calculation and following fast back substitution calculation; the Seidel equation is introduced into active iteration and reactive iteration to increase the flow computation speed.According to the application, as data is read from a data file of the given structure, the data reading speed and the calculating speed of Ipi, Iqi or delta Pi and delta Qi are greatly increased; for example, as for the IEEE-118 system, compared with the traditional method, the data file reading time, the factor table forming time, the active and reactive iteration time and the total flow calculating time are 7.31%, 3.17%, 7.51% and 7.29% of the data file reading time, the factor table forming time, the active and reactive iteration time and the total flow calculating time of the traditional method; the more nodes of the system are, the larger the advantages of the application are.

Description

technical field [0001] The invention belongs to the field of power system analysis and calculation. Background technique [0002] Power flow calculation is a basic calculation in power system, and it is the basis for steady-state analysis of power system and determination of system operation mode. The PQ decomposition method (fast decoupling method) derived from the Newton-Raphson method is an important method for power system power flow calculation. Due to a series of simplifications based on the Newton method, the PQ decomposition method has a fast convergence speed and consumes memory. Less, more suitable for real-time power flow calculation of power system. Therefore, it is the goal that people have been pursuing to further improve the speed of power flow calculation by PQ decomposition method. [0003] Three matrices Y, B′, B″ are needed in the power flow calculation of the PQ decomposition method, among which the admittance matrix Y is used to calculate the node curr...

AI Technical Summary

Problems solved by technology

The storage of more data files makes it take a long time to write data files and open data files in the power flow program to write and read data, which is not conducive to real-time calculation

[0006] (2) The storage and reading methods of Y, B′, B″ array elements are unreasonable

[0008] (3) The way of forming factor arrays for B', B" arrays is not suitable

[0010] 2) Insufficient application of sparse matrix technology when forming factor tables

B′, B″ array and Y array element structure are almost the same, and the Y array elements are extremely sparse, if the sparse matrix technology is not used when forming the factor table, the calculation efficiency will be extremely low

The application of sparse matrix technology in the triangular decomposition method is rare, while the application of sparse matrix technology in the traditional factor table method is extremely extensive, but not fully in place

[0011] (4) Inadequate application of sparse matrix technology in the process of previous generation and back generation with factor table

[0012] After the factor table is formed in the traditional method, the non-zero elements of the triangle on the factor table can be used to quickly replace and obtain Δδ i , ΔV i , but the column subscripts of non-zero elements and the number of non-zero elements in each row are stored separately so that the calculation speed is not optimal, and the previous generation of calculations for ΔP / V and ΔQ / V did not use sparse matrix technology

[0013] (5) The Seidel iteration method is not introduced in the active and reactive iteration process

[0014] The introduction of the Seidel iterative method not only has a great impact on the calculation speed of the PQ decomposition method, but may also affect its convergence sometimes, while the Seidel iterative method is rarely introduced in the traditional PQ decomposition method

[0015] Due to the above reasons, the calculation speed of the traditional PQ decomposition method is far from optimal

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