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| Tekijä: | Ahmed, Zeeshan |
| Työn nimi: | Analysis and De-Noising of Partial Discharge Signals in Medium Voltage XLPE Cables |
| Julkaisutyyppi: | Diplomityö |
| Julkaisuvuosi: | 2015 |
| Sivut: | 94+11 Kieli: eng |
| Koulu/Laitos/Osasto: | Sähkötekniikan korkeakoulu |
| Oppiaine: | Electrical Systems (S3015) |
| Valvoja: | Lehtonen, Matti |
| Ohjaaja: | Hussain, Amjad |
| Elektroninen julkaisu: | http://urn.fi/URN:NBN:fi:aalto-201603291511 |
| Sijainti: | P1 Ark Aalto 3542 | Arkisto |
| Avainsanat: | diagnostics XLPE insulated cables insulation degradation partial discharge characteristics monitoring adaptive de-noising |
| Tiivistelmä (eng): | The partial discharge (PD) measurements have been widely used in the field of insulation diagnostics. The presence of partial discharges inside the cable indicates the degradation of insulation material. This thesis deals with the development of insulation diagnostic method based on the partial discharge measurements. The useful information about the partial discharge activity and insulation defects is extracted by the experimental results. A measuring test setup was established in the high voltage laboratory. Artificial cavity was introduced inside the MV XLPE cable by using the traditional needle-plane configuration. The aim of study was to interpret the variations in the partial discharge characteristics over the insulation ageing period in terms of physical phenomenon's taking place in PD sources. The statistical characteristics formulated with the help of PRPDA technique and ultra-wideband discharge characteristics by using HFCT sensor were studied and analyzed. The variations in these characteristics allow to diagnose the insulation conditions as well as detect the type of discharge mechanisms. In the second part of thesis, detailed analytical study about the de-noising techniques has been conducted. In order to design an efficient de-noising filter for onsite and online PD monitoring system, various factors such as optimal wavelet selection, number of decomposition levels and threshold setting has been studied. An automated self-adaptive de-noising algorithm based on the frequency characteristics of partial discharge signals has been presented in this study. |
| ED: | 2016-04-17 |
INSSI tietueen numero: 53323
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