Evolutionary Bayesian Fusion for Transformers Fault Detection
Paper
Paper/Presentation Title | Evolutionary Bayesian Fusion for Transformers Fault Detection |
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Presentation Type | Paper |
Authors | Cui, Yi (Author), Naranpanawe, Lakshitha (Author) and Seo, Junhyuck (Author) |
Journal or Proceedings Title | Proceedings of the 26th Australasian Universities Power Engineering Conference (AUPEC2016) |
ERA Conference ID | 50279 |
Number of Pages | 6 |
Year | 2016 |
Place of Publication | Brisbane, Australia |
ISBN | 9781509014057 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/AUPEC.2016.07749299 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/7749299 |
Conference/Event | 26th Australasian Universities Power Engineering Conference (AUPEC 2016) |
Australasian Universities Power Engineering Conference | |
Event Details | 26th Australasian Universities Power Engineering Conference (AUPEC 2016) Event Date 25 to end of 28 Sep 2016 Event Location Brisbane, Australia |
Event Details | Australasian Universities Power Engineering Conference AUPEC |
Abstract | This paper presents an evolutionary Bayesian fusion method for transformer fault detection. It adopts Bayesian Network (BN) to explore the causal relationship between different potential faults inside transformer and fault symptoms; and then such knowledge is used to identify the fault types of transformer. Since Bayesian network acquires fault evidence gradually and transformer fault diagnosis is also an evolutionary process, the proposed method determines an optimal set of measurements, which need to be performed at each diagnostic step. This methodology can improve the accuracy of transformer fault identification while improve the efficiency of diagnostic process since the number of required measurements is minimized and only meaningful fault evidences are used in fault identification. Case studies are presented to verify the proposed method. |
Keywords | Bayesian network; Data and information fusion; Fault detection; Maximum posteriori probability estimation; Multiple source; Transformer |
ANZSRC Field of Research 2020 | 400808. Photovoltaic power systems |
400803. Electrical energy generation (incl. renewables, excl. photovoltaics) | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Queensland |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7w3v/evolutionary-bayesian-fusion-for-transformers-fault-detection
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