Power system load modeling method based on typical load group

Abstract

The invention relates to a power system load modeling method based on a typical load group. The power system load modeling method comprises the steps of: 1) establishing a typical load group model: generating the typical load group model according to node load information; 2) and establishing a node load model: performing weighted aggregation to obtain node load model parameters according to parameters of the typical load group model and proportions of typical load groups, and establishing a node load model based thereon. Compared with the typical load parameters adopted in simulation at present, the power system load modeling method obtains load parameters of the node load model parameters by performing weighted aggregation on the typical load groups, thus the node active voltage response characteristics and reactive voltage response characteristics are closer to the actual measured situation.

Description

technical field [0001] The invention relates to the field of power system and its automation, in particular to a load modeling method of a power system based on a typical load group. Background technique [0002] A large number of calculation and test results show that: the load model has a great influence on the calculation results of the dynamic behavior of the power system, and also has a certain impact on the power flow calculation, short circuit calculation, safety analysis, voltage stability, etc. In borderline cases, it is also possible to fundamentally change qualitative conclusions. [0003] The dynamic mechanism model has been widely used in practical engineering because of its clear concept and strong applicability. At present, the dynamic load models in my country's power grids at all levels use a fixed ratio of motors connected in parallel with static loads, and most of the motors use typical parameters. There are two types of sources of typical parameters gen...

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Problems solved by technology

[0005] The overall measurement and identification method does not depend on user statistical data and is simple and practical. However, it needs to install corresponding meas

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