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  1. A method to capture and de-noise partial discharge pulses using discrete wavelet transform and ANFIS
A method to capture and de-noise partial discharge pulses using discrete wavelet transform and ANFIS
1037 viewed

A method to capture and de-noise partial discharge pulses using discrete wavelet transform and ANFIS

Jahangir, H

A method to capture and de-noise partial discharge pulses using discrete wavelet transform and ANFIS

Jahangir, H ; Sharif University of Technology | 2015

1037 Viewed
  1. Type of Document: Article
  2. DOI: 10.1002/etep.1986
  3. Publisher: John Wiley and Sons Ltd , 2015
  4. Abstract:
  5. Due to the presence of excessive noise in the recorded partial discharge (PD) current signals, de-noising of these signals is a crucial task for performing any investigation on the subject. Meanwhile, to accelerate this de-noising process a single PD pulse can be extracted from the train of those recorded pulses, followed by its de-noising. In this paper a single PD pulse is extracted from the train of recorded PD pulses, using noisy recorded data cumulative energy. A de-noising technique based on adaptive neuro-fuzzy inference systems is proposed. To verify the validity of the proposed method, four different sources of PD signals are physically simulated. The proposed method is applied on laboratory recorded signals and is compared with a conventional method which is based on discrete wavelet transform (DWT) and linear adaptive filtering method. Furthermore, the proposed algorithm is used for extraction and de-noising of single PD activities of an on-site recorded data. This method introduces an efficient pulse distortion mitigation tool which requires no adjustment of any pre-set parameter - neither for different noise levels, nor for different kind of PD pulses. This is an important feature of the proposed technique
  6. Keywords:
  7. Adaptive de-noising ; PD extraction ; Discrete wavelet transforms ; Extraction ; Fuzzy inference ; Fuzzy systems ; Mathematical transformations ; Partial discharges ; Wavelet transforms ; Adaptive neuro-fuzzy inference system ; ANFIS ; Conventional methods ; De-noising ; De-noising techniques ; Important features ; Linear adaptive filtering ; Partial discharge pulse ; Signal denoising
  8. Source: International Transactions on Electrical Energy Systems ; Volume 25, Issue 11 , September , 2015 , Pages 2696-2712 ; 20507038 (ISSN)
  9. URL: http://onlinelibrary.wiley.com/doi/10.1002/etep.1986/abstract;jsessionid=E218D34D50CE61F1B01AEF774E48212C.f02t01