State Identification of Transformer under DC Bias Based on Wavelet Singular Entropy

To identify the DC bias state of transformers, a DC bias state identification method of transformer based on wavelet singular entropy is proposed in this paper. The vibration principle of transformers under DC bias has been analyzed. By combining continuous wavelet transform, singular value decomposition, and information entropy, the analysis method of wavelet singular entropy is proposed. The vibration signal of the transformer before and after DC bias is transformed by continuous wavelet transform, and then the wavelet time-frequency diagram is compared and analyzed. Furthermore, the DC bias state of the transformer is identified by wavelet singular entropy of vibration signal. The wavelet singular entropy under different states is in different numerical ranges, and the wavelet singular entropy of vibration signal under DC bias is significantly greater than that under no DC bias. The wavelet singular entropy effectively reflects the DC bias state of transformers. Then the proposed method is applied to a 500 kV transformer and a 220 kV transformer in the China Southern Power Grid. The results show that the proposed method can accurately and effectively identify the DC bias state of transformers.