How Wavelets Improve Transformer Fault Diagnosis？
During operation, transformers may generate magnetizing inrush currents due to various factors. These inrush currents not only affect the normal operation of the transformer but may also compromise the stability of the power system. Therefore, accurately identifying transformer magnetizing inrush current is crucial for effectively suppressing such inrush.
Next, let's explore how wavelet theory is applied in analyzing transformer magnetizing inrush current. Wavelet analysis is a method that provides localization in both time and frequency domains, making it highly effective in processing non-stationary signals. The fundamental idea of wavelet transform is to decompose a signal into wavelet components at different frequency and time scales, which are then analyzed and processed.
Transformer magnetizing inrush current is a transient high-current phenomenon caused by sudden changes in voltage or current. Its characteristics mainly include nonlinearity, non-stationarity, periodicity, and randomness. These features make traditional current analysis methods face significant challenges when dealing with transformer magnetizing inrush currents. In comparison, wavelet theory offers four key advantages in the analysis of transformer inrush current:
Signal Denoising: Since magnetizing inrush current signals contain substantial noise, denoising is necessary. Wavelet analysis enables multi-scale decomposition of the signal, followed by thresholding of wavelet coefficients at each scale, effectively removing noise.
Signal Reconstruction: Wavelet analysis not only denoises signals but also enables signal reconstruction. By selecting an appropriate wavelet basis function and thresholding method, it can effectively preserve the main signal features while eliminating noise.
Feature Extraction: Wavelet analysis can effectively extract features of magnetizing inrush current. By applying wavelet transform, the energy distribution of the signal across different frequency and time scales can be obtained, allowing for the identification of key signal characteristics.
Fault Diagnosis: By comparing inrush current signals under normal and faulty conditions, differences can be identified to enable fault diagnosis. Wavelet analysis effectively highlights these differences, thereby improving the accuracy of fault detection.
Wavelet theory provides a powerful tool for analyzing transformer magnetizing inrush current. Through wavelet analysis, tasks such as denoising, reconstruction, feature extraction, and fault diagnosis of inrush currents can be achieved, thereby enhancing the operational safety of transformers and the stability of power systems.
