Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151496
Title: A missing-data tolerant method for data-driven short-term voltage stability assessment of power systems
Authors: Zhang, Yuchen
Xu, Yan
Zhang, Rui
Dong, Zhao Yang
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Zhang, Y., Xu, Y., Zhang, R. & Dong, Z. Y. (2019). A missing-data tolerant method for data-driven short-term voltage stability assessment of power systems. IEEE Transactions On Smart Grid, 10(5), 5663-5674. https://dx.doi.org/10.1109/TSG.2018.2889788
Journal: IEEE Transactions on Smart Grid
Abstract: With the widespread deployment of phasor measurement units (PMU), synchronized measurements of the power system has opened opportunities for data-driven short-term voltage stability (STVS) assessment. The existing intelligent system-based methods for data-driven stability assessment assume full and complete data input is always available. However, in practice, after a fault occurs in the system, some PMU data may not be fully available due to PMU loss and/or fault-induced topology change, which deteriorates the stability assessment performance. To address this issue, this paper proposes a missing-data tolerant method for post-fault STVS assessment. The buses in the system are strategically grouped to maintain a high level of grid observability for the stability assessment model under any PMU loss and/or topology change scenario, and a structure-adaptive ensemble learning model is designed to adapt its structure to only use available feature inputs for real-time STVS assessment. By marked contrast to existing methods, the proposed method demonstrates much stronger missing-data tolerance and can maintain a high STVS assessment accuracy even when a large portion of measurements are missing.
URI: https://hdl.handle.net/10356/151496
ISSN: 1949-3053
DOI: 10.1109/TSG.2018.2889788
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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