Power load feature extraction method and system

Abstract

The invention discloses a power load characteristic extraction method and system, and the method comprises the following steps of 1, obtaining a power load curve, and processing the power load curve through the empirical mode decomposition, and obtaining a new data sequence h1 (t); step 2, acquiring a maximum information coefficient of the load characteristic X and each empirical mode decomposition component Y of the load time sequence; and step 3, carrying out correlation analysis on the load characteristic X and a target imf component according to the maximum correlation coefficient, step 4,obtaining different imf component final load feature subsets according to the new data sequence, superposing the different final load feature subsets, and obtaining a load feature set TBEST. According to the method and the system, the original power load time sequence is decomposed by using the empirical mode decomposition, and then the power load characteristics are screened by using the maximumcorrelation coefficient of the load characteristics and the load components, so that compared with the prior art, the precision of the power load characteristic prediction is greatly improved, and the calculated amount is effectively reduced.

Description

technical field [0001] The invention relates to the field of electric power, in particular to a method and system for extracting electric load features. Background technique [0002] Electric load is an important part of the power system, as a consumer of electric energy, it has an important impact on the analysis, design and control of the power system. Electric load feature extraction is the key premise of load forecasting. Since the power load is easily affected by many factors, and with the large-scale access of energy storage, electric vehicles, and renewable distributed power sources, it is difficult to extract the characteristics of the power load. In order to cope with the challenges brought by the complex environment, the decomposition of the original load time series and the correlation analysis of the load influencing factors have been deeply studied at home and abroad. Scholars at home and abroad have also proposed many methods in order to accurately extract lo...

AI Technical Summary

Problems solved by technology

Scholars at home and abroad have also proposed many methods in order to accurately extract load characteristics and realize accurate classification of load characteristics, but these methods have defects of varying degrees, such as the method of extracting the time and season of load characteristic sample collection as feature vectors, It cannot reflect the essential characteristics of the dynamic characteristics of the load and is highly subjective; taking the parameters of the induction motor comprehensive model as the feature vector and taking the model response under the standard voltage excitation as the feature vector are two commonly used feature extraction methods now, but Both methods must select the model structure and carry out parameter identification to obtain the load dynamic characteristic eigenvector, the error of model structure and parameter identification will be unavoidable, and the amount of calculation will be large

Images