Research On Improvement Of Classical Algorithms For Active Power Optimization In Power Systems

The primary requirement for the economic operation of the power system is to ensure that the entire power system is safe,stable,and reliable,and that the power quality meets standards,to improve the efficiency of power transmission and production as much as possible and reduce the cost of power supply.With the increasing scale of the power sys-tem,further requirements have been imposed on active economic dispatch.This paper cuts through the mathematical model of the power system node network,further studies the storage method of admittance matrix in the power system,how to make better use of the sparse matrix method and what needs to be improved when solving linear equations.A new fast solution is proposed.The Gaussian Jordan elimination factor table method of the node impedance matrix is fully utilized.Analyze the traditional Gaussian elimination method and the shortcomings of the rapid factor table method men-tioned in the literature,as well as the deficiencies in the calculation process.Combine and improve the multiple methods for solving linear equations.Compare with the im-proved algorithm.Introduce power flow calculation and apply sparse matrix technology.Through various methods of calculating the network loss micro-increasing rate,such as the polar and rectangular coordinate forms of the impedance matrix method,the polar and rectangular coordinate forms of the admittance matrix method and so on,the transposed elegant The calculation principles and calculation steps of the compara-ble matrix method are analyzed in detail or deduced,and a variety of methods are com-pared with each other for calculation and optimization calculations.The feasibility of introducing sparse matrix technology and the new Gaussian Jordan elimination factor table method is analyzed.Superiority.Through the system test and calculation of IEEE nodes,the feasibility of various calculation methods for solving the network loss micro-increasing rate.The sparse matrix technology and the new Gaussian Jordan elimination factor table method are used to improve the calculation process Verification of the ef-ficiency of the conclusion.