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Data-Driven Joint Voltage Stability Assessment Considering Load Uncertainty: A Variational Bayes Inference Integrated With Multi-CNNs.
- Source :
-
IEEE Transactions on Power Systems . May2022, Vol. 37 Issue 3, p1904-1915. 12p. - Publication Year :
- 2022
-
Abstract
- Few studies have focused on assessing the transient and steady-state voltage stability status of dynamic systems simultaneously. This motivated us to propose a new concept referred to as joint voltage stability assessment (JVSA). Towards this end, this paper proposes a novel data-driven JVSA method considering load uncertainty. It combines multiple convolutional neural networks (multi-CNNs) and a novel variational Bayes (VB) inference for better JVSA accuracy. First, the multi-CNN model is utilized to fast estimate the maximum voltage deviations during the transient and steady-state process. Uncertain load scenarios and system topology under $N$ -1 contingency with are chosen as inputs of each CNN model. Second, estimated voltage deviations are put into the VB inference to automatically infer the transient and steady-state voltage stability status. To validate its effectiveness, numerical simulations are performed on the modified WECC 179-bus system by comparing with benchmark algorithms. It is demonstrated that the proposed data-driven JVSA method is more accurate and faster than the conventional VSA method. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08858950
- Volume :
- 37
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Power Systems
- Publication Type :
- Academic Journal
- Accession number :
- 156419396
- Full Text :
- https://doi.org/10.1109/TPWRS.2021.3111151