Details
| Original language | English |
|---|---|
| Title of host publication | VDE-Fachtagung Hochspannungstechnik 2018 |
| Publisher | VDE Verlag GmbH |
| Pages | 270-275 |
| Number of pages | 6 |
| ISBN (electronic) | 9783800748075 |
| Publication status | Published - 2018 |
| Event | VDE-Fachtagung Hochspannungstechnik 2018 - VDE Conference on High Voltage Technology 2018 - Berlin, Germany Duration: 12 Nov 2018 → 14 Nov 2018 |
Publication series
| Name | VDE-Fachtagung Hochspannungstechnik 2018 |
|---|
Abstract
Power transformers are key elements of a power system. Monitoring systems and condition-based maintenance is applied to ensure their reliability. Partial discharges (PD) lead to a progressive deterioration of the transformer's insulation system. Therefore, detecting and localizing PDs in early stages enables the operators to have a maintenance plan for the transformer and extend its lifetime. This paper is aimed to detect and localize PDs using ultrahigh frequency (UHF) measurement technique. The main challenge in UHF PD based measurement for localization is a precise determination of signal arrival time in noisy batch data. The more precise determination of PD signal onset, the more exact localization is possible. An algorithm is proposed for this purpose, in which two methods are introduced to detect the arrival time of the signals received by the UHF probes. This algorithm is based on receiving several signal sets and a detailed discussion is presented on how the accuracy of the localization algorithm can be affected by increasing the number of evaluated PD-signal sets. Finally, the efficiency of the proposed algorithm is evaluated by modelling possible scenarios that represent the propagation of the Electromagnetic (EM) waves emitted due to the PDs through paper insulation and the windings in a power transformer. Hereby, a PD fault model is placed inside a transformer tank and different barriers are placed around it to evaluate the localization accuracy under various circumstances.
ASJC Scopus subject areas
- Energy(all)
- Energy Engineering and Power Technology
- Engineering(all)
- Electrical and Electronic Engineering
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VDE-Fachtagung Hochspannungstechnik 2018. VDE Verlag GmbH, 2018. p. 270-275 (VDE-Fachtagung Hochspannungstechnik 2018).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A novel algorithm for UHF-based partial discharge localization in power transformers
AU - Ariannik, Mohamadreza
AU - Akbari Azirani, Mohammad Esmaeil
AU - Werle, Peter
AU - Akbari Azirani, Asghar
N1 - Publisher Copyright: © VDE VERLAG GMBH · Berlin · Offenbach Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - Power transformers are key elements of a power system. Monitoring systems and condition-based maintenance is applied to ensure their reliability. Partial discharges (PD) lead to a progressive deterioration of the transformer's insulation system. Therefore, detecting and localizing PDs in early stages enables the operators to have a maintenance plan for the transformer and extend its lifetime. This paper is aimed to detect and localize PDs using ultrahigh frequency (UHF) measurement technique. The main challenge in UHF PD based measurement for localization is a precise determination of signal arrival time in noisy batch data. The more precise determination of PD signal onset, the more exact localization is possible. An algorithm is proposed for this purpose, in which two methods are introduced to detect the arrival time of the signals received by the UHF probes. This algorithm is based on receiving several signal sets and a detailed discussion is presented on how the accuracy of the localization algorithm can be affected by increasing the number of evaluated PD-signal sets. Finally, the efficiency of the proposed algorithm is evaluated by modelling possible scenarios that represent the propagation of the Electromagnetic (EM) waves emitted due to the PDs through paper insulation and the windings in a power transformer. Hereby, a PD fault model is placed inside a transformer tank and different barriers are placed around it to evaluate the localization accuracy under various circumstances.
AB - Power transformers are key elements of a power system. Monitoring systems and condition-based maintenance is applied to ensure their reliability. Partial discharges (PD) lead to a progressive deterioration of the transformer's insulation system. Therefore, detecting and localizing PDs in early stages enables the operators to have a maintenance plan for the transformer and extend its lifetime. This paper is aimed to detect and localize PDs using ultrahigh frequency (UHF) measurement technique. The main challenge in UHF PD based measurement for localization is a precise determination of signal arrival time in noisy batch data. The more precise determination of PD signal onset, the more exact localization is possible. An algorithm is proposed for this purpose, in which two methods are introduced to detect the arrival time of the signals received by the UHF probes. This algorithm is based on receiving several signal sets and a detailed discussion is presented on how the accuracy of the localization algorithm can be affected by increasing the number of evaluated PD-signal sets. Finally, the efficiency of the proposed algorithm is evaluated by modelling possible scenarios that represent the propagation of the Electromagnetic (EM) waves emitted due to the PDs through paper insulation and the windings in a power transformer. Hereby, a PD fault model is placed inside a transformer tank and different barriers are placed around it to evaluate the localization accuracy under various circumstances.
UR - http://www.scopus.com/inward/record.url?scp=85069920104&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85069920104
T3 - VDE-Fachtagung Hochspannungstechnik 2018
SP - 270
EP - 275
BT - VDE-Fachtagung Hochspannungstechnik 2018
PB - VDE Verlag GmbH
T2 - VDE-Fachtagung Hochspannungstechnik 2018 - VDE Conference on High Voltage Technology 2018
Y2 - 12 November 2018 through 14 November 2018
ER -