A sequential Bayesian approach to online power quality anomaly segmentation
Jiang, Xu and Stephen, Bruce and McArthur, Stephen (2020) A sequential Bayesian approach to online power quality anomaly segmentation. IEEE Transactions on Industrial Informatics. ISSN 1551-3203 (https://doi.org/10.1109/TII.2020.3003979)
Preview |
Text.
Filename: Jiang_etal_IEE_TII_2020_A_sequential_Bayesian_approach_to_online_power.pdf
Accepted Author Manuscript Download (964kB)| Preview |
Abstract
Increased observability on power distribution networks can reveal signs of incipient faults which can develop into costly and unexpected plant failures. While low-cost sensing and communications infrastructure is facilitating this, it is also highlighting the complex nature of fault signals, a challenge which entails precisely extracting anomalous regions from continuous data streams before classifying the underlying fault signature. Doing this incorrectly will result in capture of uninformative data. Extraction processes can be confounded by operational noise on the network including harmonics produced by embedded generation. In this paper, an online model is proposed. Our Bayesian Changepoint Power Quality anomaly Segmentation allows automated segmentation of anomalies from continuous current waveforms, irrespective of noise. Demonstration of the effectiveness of the proposed technique is carried out with operational field data as well as a challenging simulated network, highlighting the ability to accommodate noise from typical network penetration levels of power electronic devices.
ORCID iDs
Jiang, Xu ORCID: https://orcid.org/0000-0001-9123-9911, Stephen, Bruce ORCID: https://orcid.org/0000-0001-7502-8129 and McArthur, Stephen ORCID: https://orcid.org/0000-0003-1312-8874;-
-
Item type: Article ID code: 72866 Dates: DateEvent22 June 2020Published22 June 2020Published Online13 June 2020AcceptedNotes: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Subjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 24 Jun 2020 11:15 Last modified: 11 Nov 2024 12:44 URI: https://strathprints.strath.ac.uk/id/eprint/72866