Overvoltage identification method based on time-frequency matrix singular value

Abstract

The invention discloses an overvoltage identification method based on a time-frequency matrix singular value. The overvoltage identification method comprises the following steps of obtaining an overvoltage signal; preprocessing the overvoltage signal; decomposing the preprocessed overvoltage signal, and obtaining signal components of different frequency bands; according to the signal components of the different frequency bands, constructing a multi-scale time-frequency matrix; conducting singular value decomposition on the matrix, and calculating the singular values in different stages; and according to the weighted energy contribution rate of the singular values of the different stages, selecting characteristic quantity of overvoltage. The overvoltage identification method extracts characteristic parameters which effectively reflect the essence of the switching overvoltage according to wave shapes of transient state switching overvoltage obtained by monitoring equipment, and therefore achieves effective identification on the switching overvoltage of a power grid.

Description

technical field [0001] The invention relates to the technical field of overvoltage type identification in electric power systems, and more specifically, relates to an overvoltage identification method based on a singular value of a time-frequency matrix. Background technique [0002] Modern society has higher and higher requirements for the reliability of power system power supply. Operation experience and research show that overvoltage is one of the main causes of grid insulation damage, and it is also a decisive factor in selecting the insulation strength of electrical equipment. With the construction and development of the grid, the voltage level and transmission capacity continue to increase. In particular, operating overvoltage has become an important reason restricting the development of EHV and UHV transmission lines. Timely detection of various operating overvoltages of the power system, rapid and accurate distinction of fault types, and the establishment of an effe...

AI Technical Summary

Problems solved by technology

Traditional Fourier analysis lacks the ability to analyze signals in the time domain, and it also has shortcomings such as spectrum leakage. It is not suitable for analyzing non-stationary signals such as operating overvoltages; there is no unified standard for the selection of structural elements in mathematical morphology, and the extraction effect It has a great relationship with artificial experience; S transform is the development of continuous wavelet transform and short-time Fourier transform, and has good time-frequency characteristics. After S transform, the signal is a high-dimensional complex matrix, and the selection of feature quantities needs further study. And the calculation efficiency is low; wavelet transform has good multi-scale analysis characteristics, can provide the characteristics of signals at different scales, and is suitable for analyzing various transient and sudden signals, so it is widely used in power system signal fault detection and location, lightning overvoltage Identification, transmission line fault line selection, power quality disturbance classification, etc., and achieved certain results

However, after wavelet decomposition, the coefficients are huge and the information is redundant. In the usual fault detection and type identification, some feature quantities have a high dimension, which makes the classifier more complicated.

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