Dissertation
Dissertation > Industrial Technology > Electrotechnical > High Voltage Engineering > High voltage test equipment and measurement techniques > High - voltage measurement technique

Research on Noise Suppression in Partial Discharge On-Line Monitoring

Author WangZhiBin
Tutor ZuoHuaiGang
School Yanshan University
Course Control Theory and Control Engineering
Keywords Partial discharge Denoising Frequency domain filtering Wavelet Transform Threshold function
CLC TM835
Type Master's thesis
Year 2010
Downloads 119
Quotes 1
Download Dissertation

Partial discharge within the large electrical equipment insulation defects have close contact , timely line partial discharge monitoring to determine the the transformer internal insulation state , has an important significance to prevent the the power transformers accident , to protect the safe and stable operation of power system . Achieve online monitoring key is how strong noise detected a weak partial discharge signal interference identification and suppression has been the difficulty of line partial discharge monitoring need to be resolved . Interference suppression methods and ideas a lot, but the real success for the monitoring system is not much , and the result is not satisfactory . With the development of modern digital processing techniques , interference suppression measures line partial discharge monitoring is tending to develop in the direction software . In this paper, a narrow-band interference and white noise suppression method , frequency domain filtering method for the continuous cycle amplitude signal several times higher than the partial discharge narrow-band interference . In this paper, the design of the two classes associated IIR lattice notch filter simulation results show that this method can effectively eliminate partial discharge signal the continuous cycle narrowband interference . White noise has the characteristics of a good time-frequency analysis wavelet transform for processing various denoising method based on wavelet transform after using wavelet thresholding denoising . Are discussed further in the choice of the threshold function in order to achieve better denoising effect , a two- variable threshold function , so that the method has been significantly improved . The bivariate threshold function improved , to overcome the the hard threshold discontinuity and soft threshold method bias shortcomings . This method, change the value of the parameter k is a function of the threshold value can change between the hard and soft threshold function is used to adjust the constant deviation between the threshold wavelet coefficient to the original wavelet coefficients ; variable parameter m is used to adjust the smooth transition zone within the curve of the order of , for local fine-tuning . The simulated signal denoising research , combined with a signal-to-noise ratio (SNR) and evaluation of waveform similarity (NCC) , a comprehensive evaluation of the improved threshold function denoising effect , the results show that : by adjusting two variable parameters , improved threshold function can be obtained with the best signal-to-noise ratio and the minimum mean square error denoising effect .

Related Dissertations
More Dissertations