Partial discharge identification by using signal processing techniques
Partial Discharge (PD) detection after denoising, characterization and identification are the three main signal processing requirements of PD analysis. Voluminous digital PD data are nowadays readily available with constant improvements in PD measurement techniques. Power Engineers may be able to de...
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| Format: | Thesis |
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2008
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| Online Access: | https://hdl.handle.net/10356/4133 |
| _version_ | 1826113300053622784 |
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| author | Chia, Tze Keong |
| author2 | Sivaswamy Birlasekaran |
| author_facet | Sivaswamy Birlasekaran Chia, Tze Keong |
| author_sort | Chia, Tze Keong |
| collection | NTU |
| description | Partial Discharge (PD) detection after denoising, characterization and identification are the three main signal processing requirements of PD analysis. Voluminous digital PD data are nowadays readily available with constant improvements in PD measurement techniques. Power Engineers may be able to detect prominent PDs using oscilloscope and existing couplers. But identification of the types of developing and random occurring PD is a real challenge to any practicing engineer. In this thesis, details on using wavelet transform in the form of either continuous wavelet transform or discrete wavelet transform with two methods to denoise, identify the location of PD and retrieve PD wave shape without magnitude distortion are presented. To identify the type of PD, some experimental studies and about six existing and developed signal processing methods are carried out. Laboratory experimental study provided reproducible data with enough number of sampled points on three types of pure PD and one multisources PD. |
| first_indexed | 2024-10-01T03:20:56Z |
| format | Thesis |
| id | ntu-10356/4133 |
| institution | Nanyang Technological University |
| last_indexed | 2024-10-01T03:20:56Z |
| publishDate | 2008 |
| record_format | dspace |
| spelling | ntu-10356/41332023-07-04T16:43:57Z Partial discharge identification by using signal processing techniques Chia, Tze Keong Sivaswamy Birlasekaran School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Partial Discharge (PD) detection after denoising, characterization and identification are the three main signal processing requirements of PD analysis. Voluminous digital PD data are nowadays readily available with constant improvements in PD measurement techniques. Power Engineers may be able to detect prominent PDs using oscilloscope and existing couplers. But identification of the types of developing and random occurring PD is a real challenge to any practicing engineer. In this thesis, details on using wavelet transform in the form of either continuous wavelet transform or discrete wavelet transform with two methods to denoise, identify the location of PD and retrieve PD wave shape without magnitude distortion are presented. To identify the type of PD, some experimental studies and about six existing and developed signal processing methods are carried out. Laboratory experimental study provided reproducible data with enough number of sampled points on three types of pure PD and one multisources PD. MASTER OF ENGINEERING (EEE) 2008-09-17T09:45:10Z 2008-09-17T09:45:10Z 2005 2005 Thesis Chia, T. K. (2005). Partial discharge identification by using signal processing techniques. Master’s thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/4133 10.32657/10356/4133 Nanyang Technological University application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Chia, Tze Keong Partial discharge identification by using signal processing techniques |
| title | Partial discharge identification by using signal processing techniques |
| title_full | Partial discharge identification by using signal processing techniques |
| title_fullStr | Partial discharge identification by using signal processing techniques |
| title_full_unstemmed | Partial discharge identification by using signal processing techniques |
| title_short | Partial discharge identification by using signal processing techniques |
| title_sort | partial discharge identification by using signal processing techniques |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing |
| url | https://hdl.handle.net/10356/4133 |
| work_keys_str_mv | AT chiatzekeong partialdischargeidentificationbyusingsignalprocessingtechniques |