A novel de‐noising method based on coherence average for ultrasonic signal of partial discharge in transformer

Abstract

Abstract Ultrasonic method is widely used for the detection and location of partial discharge (PD) in transformer, however, the measured ultrasonic signal is usually corrupted by noise, and sometimes is even buried by noise entirely. Therefore, the de‐noising of the measured signal is essential. Conventional de‐nosing methods, such as wavelet method and singular value decomposition (SVD) method, generally require empirical parameter selection or estimation, and the best parameters for de‐noising vary with the PD source and noise condition, which will bring some limitation to their applicability. More importantly, conventional de‐noising methods usually have poor performance for the low signal‐to‐noise‐ratio (SNR) signals. To improve this problem, the paper proposes a de‐noising method based on coherence average for the ultrasonic signal. The de‐noising performance of the proposed method is evaluated based on a comparison with conventional methods, the results indicate that the proposed method has great de‐noising effect for the ultrasonic signals, even for the low‐SNR signals. Besides, the proposed method is free from empirical parameter selection or estimation, which can make its applicability more extensive. The proposed method can offer a practical and effective solution to the de‐noising of ultrasonic signal of PD in transformer.

Keywords

• Signal processing and detection
• Computer vision and image processing techniques
• Integral transforms
• Other topics in statistics