Composite power quality disturbance identification method based on segmented improved S transformation and random forest

Abstract

A composite power quality disturbance identification method based on the segmented improved S transformation and a random forest comprises the following steps of firstly, segmenting an improved S transformation frequency domain based on the disturbance signal characteristics, and assigning different window width adjustment factor values to each segment; secondly, extracting the disturbance signalcharacteristics according to the frequency distribution characteristics of different frequency bands, constructing an RF classifier based on a CART algorithm by means of the extracted disturbance signal characteristics, and classifying the signals to be detected. According to the composite power quality disturbance identification method based on the segmented improved S transformation and the random forest, the method has higher classification precision for the most single power quality disturbance signals and the common double composite power quality disturbance signals and has better noise robustness, and the generalization error of the constructed classifier is lower.

Description

technical field [0001] The invention relates to the field of power quality analysis, in particular to a composite power quality disturbance identification method based on segmented improved S transform and random forest. Background technique [0002] S-transform is a time-frequency analysis tool widely used in power quality disturbance (PQD) signal detection and classification. It inherits and develops the theory of wavelet transform and short-time Fourier transform, adopts Gaussian window function and the window width is proportional to the reciprocal of frequency, which eliminates the choice of window function and improves the defect of fixed window width, and its extraction The feature quantity is not sensitive to noise and has certain anti-noise ability. In recent years, many scholars have adopted S-transform and combined with other analysis tools in the analysis of PQD, resulting in a large number of research results. However, its resolution in different frequency dom...

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Problems solved by technology

[0004] In order to solve the problem that the classification accuracy is not high enough and the noise robustness is not strong enough when the method based on S transform and random forest is used in the identification of composite power quality disturbance in the prior art

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