Winding Condition Monitoring of Power Transformer Based on the optimized TQWT
- Resource Type
- Conference
- Authors
- Wang, Shan; Qian, Guochao; Dai, Weiju; Hong, Zhihu; Wang, Fenghua; Cheng, Deliang
- Source
- 2022 IEEE 5th International Electrical and Energy Conference (CIEEC) Electrical and Energy Conference (CIEEC), 2022 IEEE 5th International. :2256-2260 May, 2022
- Subject
- Engineered Materials, Dielectrics and Plasmas
Power, Energy and Industry Applications
Vibrations
Wavelet transforms
Q-factor
Condition monitoring
Windings
Wavelet analysis
Entropy
Singular spectrum entropy
power transformer
tunable Q-factor wavelet transform (TQWT)
vibration signals
winding deformation
- Language
How to accurately judge the winding condition after the sudden short-circuit impacts is an essential issue for the secure and reliable operation of power system. Since the vibration signals of transformer tank are mainly caused by winding vibration under the short-circuit impacts, this study is focused to recognize the winding condition by using the vibration signals decomposed by the tunable Q-factor wavelet transform (TQWT) with the optimized parameters of the Q-factor and redundancy according to the singular spectrum entropy. With the obtained sub-bands energy of vibration signals, the index of standard deviation (SD) and average power (AP) are defined to accurately recognize the winding condition of transformer. The short-circuit impact experiment is made for a real 110kV transformer with different short-circuit impact currents. The calculated results that the proposed method can better decompose the transient vibration signals and the judgement of winding condition of transformer are agreed well with its real mechanical condition.