Feature extracting method for power load dynamic features

Abstract

The invention discloses a feature extracting method for power load dynamic features. The method comprises the steps of S1, preprocessing the three-phase transient state current data reflecting the power load dynamic features, and selecting the sampling data in one cycle before a fault and in two cycles after the fault as samples; S2, selecting a wavelet basis, and determining the lifting scheme of the wavelet basis; S3, selecting a resolving layer number m and performing m-layer resolving on the three-phase transient state current data of each sample by utilizing the wavelet packet conversion with the lifting scheme; S4, selecting the wavelet packet to reconstruct original signals, wherein the signal number in the wavelet packet is marked as r; and S5, extracting a corresponding reconstruction coefficient and calculating an energy value Mij(k) as the sample number, wherein j is equal to 1, 2 and 3 respectively representing the three phases of currents, k is equal to 1, 2, 3, ellipsis and r represents a frequency band. The multiplication operation number of the wavelet packet conversion based on the lifting scheme is reduced by about a half, so a faster conversion speed is realized.

Description

technical field [0001] The invention relates to a feature extraction method of electric load dynamic characteristics. Background technique [0002] The load model is one of the most important factors that affect the accuracy and reliability of power system digital simulation results. However, due to the difficulty of load modeling itself, power system load modeling has always been an unsolved problem in power system analysis and calculation. For load modeling, in order to accurately reflect the load characteristics, different load nodes should have different load models at different times. A general model covering all load characteristics creates a contradiction between the engineering practicability of load modeling and the accuracy of the model. The main way to solve this contradiction is the classification and synthesis of load dynamic characteristics. [0003] The classification and synthesis of load dynamic characteristics refers to grouping the dynamic load disturbanc...

AI Technical Summary

Problems solved by technology

[0004] In order to accurately extract the eigenvectors that reflect the dynamic characteristics of the load and realize the accurate classification of the dynamic characteristics of the load, in-depth research has been carried out at home and abroad, and many methods have been proposed, but these methods have different degrees of defects, such as the extraction of load characteristics sample collection The time and season as the eigenvector method cannot reflect the essential characteristics of the dynamic characteristics of the load, and has greater subjectivity; the parameters of the induction motor comprehensive model as the eigenvector and the model response under the standard voltage excitation as the eigenvector are now There are two commonly used feature extraction methods, but both methods must select the model structure and perform parameter identification to obtain the load dynamic characteristic vector. Model structure errors and parameter identification errors will be inevitable, and the amount of calculation will be large

In addition, for the model response under the standard voltage excitation as the eigenvector, since the voltage drop amplitude of the standard voltage excitation is generally different from the voltage drop amplitude of each load disturbance data, it is affected by the interpolation and extrapolation capabilities of the selected load model, There will be some deviation in the model response under the standard voltage excitation

Images