Power Quality Disturbance Detection Based on Improved Robust Random Cut Forest
Paper
Paper/Presentation Title | Power Quality Disturbance Detection Based on Improved Robust Random Cut Forest |
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Presentation Type | Paper |
Authors | Zhang, Ge, Bai, Feifei, Cui, Yi, Dart, David, Yaghoobi, Jalil and Zillmann, Matthew |
Journal or Proceedings Title | Proceedings of 2022 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia) |
Number of Pages | 6 |
Year | 2022 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | China |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICPSAsia55496.2022.9949860 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/abstract/document/9949860?casa_token=aFiEl8ykkOsAAAAA:Mfqx3LRS7hX7xsXFjkRg0WFuipxVlb72BTkCTy2AIcQtaSCfQjFFo4vA6-_CKyuIU10KJOpLxzs |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/9948701/proceeding |
Conference/Event | 2022 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia) |
Event Details | 2022 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia) Parent IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia) Delivery In person Event Date 08 to end of 11 Jul 2022 Event Location Shanghai, China |
Abstract | Many distribution network disturbances exhibit unique electrical signatures which can be observed from voltage and current waveforms. With the continuous enhancement of Power Quality Data (PQD) acquisition capabilities, it is feasible to continuously monitor the operating status of power nodes from a more detailed perspective. In the past decade, an increasing amount of high-quality PQD has been collected accumulating a massive and unique high-resolution power grid data asset. However, how to obtain valuable information, such as Power Quality Disturbance Events (PQDEs) from massive PQD remains challenging in the research community. In this paper, a reliable PQDEs detection method is proposed based on the improved Robust Random Cut Forest (RRCF). This method achieves accurate detection of PQDEs through the adaptive improvement of pre-filtering based on Ensemble Empirical Mode Decomposition (EEMD) and redundant interpolation. Numerical test results on the synthetic PQD and the realistic pollution experiment of a silicone rubber insulator in a salt fog chamber demonstrate the reliability, efficiency, and scalability of the proposed approach in practical online detection. |
Keywords | power quality; Robust Random Cut Forest (RRCF); abnormality detection; big data; arcing |
ANZSRC Field of Research 2020 | 400803. Electrical energy generation (incl. renewables, excl. photovoltaics) |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | University of Queensland |
Griffith University | |
NOJA Power, Australia | |
Energy Queensland, Australia |
https://research.usq.edu.au/item/x8zv7/power-quality-disturbance-detection-based-on-improved-robust-random-cut-forest
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