Identification-increasing-degree super-regression load-modeling multi-curve fitting model based on support vector machine

Abstract

The invention discloses an identification-increasing-degree super-regression load-modeling multi-curve fitting model based on a support vector machine. The model includes a single training set fittingfunction creating module, a multi-increment learning set fitting function creating module, an extrapolation prediction learning set creating module, a minimum vector spacing optimizing module and a multi-curve fitting function creating module; the single training set fitting function creating module is oriented to a single curve observation value training set and based on an algorithm of a support vector regression machine and obtains fitting functions of non-linear objects; the multi-increment learning set fitting function creating module is oriented to multiple curve observation value training sets and obtains multiple corresponding fitting functions; the extrapolation prediction learning set creating module is used for creating sets of all output vectors as identification increasing degree learning sets; the minimum vector spacing optimizing module adopts the minimum vector spacing as an optimizing index for searching for an aggregation center of the identification increasing degree learning sets on the basis of an identification increasing degree learning set, and the center serves as a data training set representing comprehensive characters of all curves; the multi-curve fitting function creating module obtains fitting functions involving the basic features of all the curves. The model based on the support vector machine can achieve the purpose of making local feature identification degree gradually approach to whole feature identification degree in a fitting process.

Description

technical field [0001] The invention relates to the technical field of electric load models. Background technique [0002] The power load model is very important in the simulation analysis and calculation of the power grid, and the load model has a great influence on the stability calculation results. However, due to the complexity, time-varying and distribution characteristics of the power load, it is very difficult to model the power system load. The parameter identification algorithm based on single curve fitting can only include part of the characteristics of the load model. The final determination of the load model depends on the increasing number of curves, and the load model characteristic information in various situations is integrated to continuously correct the load model. Only in this way can iteratively approach the real load model. [0003] Support vector regression machine is an effective algorithm for nonlinear fitting of single curve, and the obtained curve...

AI Technical Summary

Problems solved by technology

[0005] In the existing technology, it is desirable to have multiple curves measured for the same load group, but the curve functions of the multiple curves are different; in addition, in the process of data acquisition, the curve data for specific loads is less , there are many collection devices installed in the power grid, and there are more curve data obtained from different nodes on different power grids

Images