Load modeling method and system based on comprehensive information theory and modern interior point theory

Abstract

The invention provides a load modeling method and a loading modeling system based on a comprehensive information theory and a modern interior point theory. The method comprises the following steps that the load data of a plurality of transformer substations is counted; the load characteristic index of each transformer substation is calculated according to the counted load data; each transformer substation is classified by a clustering method according to the load characteristic indexes obtained through calculation, and in addition, the typical transformer substation is selected; a synthetic load model is built for the selected typical transformer substation; and the modern interior point theory is utilized for identifying the parameters to be solved of the synthetic load model, and an optimal synthetic load model is built. The load modeling method and the loading modeling system have the advantages that precise parameter identification results can be effectively and fast obtained in the identification of the load model parameters, so the accuracy of the built load model is improved.

Description

technical field [0001] The invention relates to the technical field of electric power, in particular to a load modeling method and system based on comprehensive information theory and modern internal point theory. Background technique [0002] With the continuous expansion of the scale of the power grid, its complexity is getting higher and higher, the dynamic stability and voltage stability of the power grid are more prominent, and the impact of the load model on the digital simulation of the power system and its safe and stable operation is also becoming more and more significant. [0003] As one of the three major components of the power system, the load is the comprehensive object of many electric equipment and users. The unknown response characteristics under the excitation of voltage and frequency changes make its modeling much more difficult than the modeling of components and systems with a single fixed structure such as generators and excitation systems. Research s...

AI Technical Summary

Problems solved by technology

However, traditional algorithms such as the least squares method have high requirements on the initial value, the algorithm has poor robustness, and it is easy to fall into local extreme points, resulting in many iterations and even entering an infinite loop.

Artificial intelligence algorithms such as genetic algorithm can overcome the defects of traditional identification methods, but introduce a large amount of calculation, resulting in long calculation time, difficult to deal with and optimize for problems with high dimensions, and the algorithm has limited ability to explore new spaces , it is easy to converge to a local optimal solution

It can be seen that the existing techniques and methods of model parameter identification are difficult to obtain accurate parameters, thus reducing the accuracy of the load model

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