Ceemdan-Mral Transformer Vibration Signal Fault Diagnosis Method Based on FBG

To solve the problem in which the vibration signal of the transformer is affected by noise and electromagnetic interference, which leads to the low accuracy of fault diagnosis pattern recognition. A CEEMDAN-MRAL fault diagnosis method based on Fiber Bragg grating (FBG) is proposed to evaluate the vibration fault state of the transformer quickly and accurately. The FBG sends the wavelength change of the optical signal center caused by the vibration of the transformer to the demodulation system, which obtains the vibration signal and effectively avoids the noise influence caused by strong electromagnetic interference inside the transformer. The vibration signal is decomposed into several intrinsic mode functions (IMFs) by complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN), and the wavelet threshold denoising algorithm improves the signal-to-noise ratio (SNR) to 1.6 times. The Markov transition field (MTF) is used to construct a training and test set. The unique MRAL-Net is proposed to extract the spatial features of the signal and analyze the time series dependence of the features to improve the richness of the signal feature scale. This proposed method effectively removes the noise interference. The average accuracy of fault diagnosis of the transformer winding core reaches 97.9375%, and the time taken on the large-scale complex training set is only 1705s, which has higher diagnostic accuracy and shorter training time than other models.