CEEMD-improved wavelet threshold denoising-based transformer winding ultrasonic detection three-dimensional imaging method

Abstract

The invention relates to a CEEMD-improved wavelet threshold denoising-based transformer winding ultrasonic detection three-dimensional imaging method, and belongs to a transformer winding deformationdetection method. The method comprises the following steps: firstly, performing CEEMD decomposition on a target signal to obtain a multi-order IMF component, then calculating a correlation coefficientof the IMF component, performing improved wavelet threshold processing on a high-frequency component with a relatively low correlation coefficient, and finally reconstructing a denoised component, alow-frequency component and a residual component to obtain a denoised signal. According to the method, most noise is suppressed while low-amplitude effective information and high-frequency effective information are reserved, and the denoising effect is ideal. In an ultrasonic detection three-dimensional imaging system, the denoised transformer winding state diagram is better in visual effect and clearer in fault position, and it is shown that the transformer winding state diagram denoising method has a good denoising effect.

Description

technical field [0001] The invention belongs to a deformation detection method of a transformer winding, in particular to a noise-removing three-dimensional imaging method for ultrasonic detection of a transformer winding. Background technique [0002] As an important substation equipment, power transformers suffer from short-circuit faults during operation, and their internal windings are affected by electromotive forces, which easily cause winding deformation. With the increase of the service life of the transformer, the deformation will be aggravated due to the accumulation effect. If the danger is not detected and eliminated in time, it will pose a huge threat to the stability of the power grid. Therefore, transformer winding deformation detection is an effective measure to ensure the reliable operation of transformers. [0003] In recent years, the commonly used winding deformation detection methods are mainly offline methods, mainly including short-circuit impedance m...

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Problems solved by technology

Wavelet transform has great advantages in random noise filtering due to its advantages of multiple decomposition scales and decorrelation, but it has defects such as difficult selection of threshold function and variable number of decomposition layers; Empirical Mode Decomposition (EMD) ) has a strong advantage in non-stationary signal processing, but it is prone to modal aliasing effect, the decomposition process is unstable, the denoising ability is poor, and it is easy to filter out low-amplitude useful information

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