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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.23204 |
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| _version_ | 1866918467825827840 |
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| author | Deng, Chao Zhu, Lipeng Liu, Chang Zhai, Hefeng Tian, Baoye Zhu, Zexiang Li, Jiayong Zhang, Cong |
| author_facet | Deng, Chao Zhu, Lipeng Liu, Chang Zhai, Hefeng Tian, Baoye Zhu, Zexiang Li, Jiayong Zhang, Cong |
| contents | The emerging deep learning (DL) technology has recently exhibited great potential in data-driven short-term voltage stability (SVS) assessment of complex power grids. However, without sufficient attention to the time-varying topological structures of today's power grids, the majority of existing DL-based SVS assessment schemes could experience severe performance degradation in practice. To address this drawback, this paper proposes an adaptive spatial-temporal graph learning-enabled SVS assessment approach that can adapt well to various topological changes. First, considering the time-varying topological conditions of a given power grid, an adaptive graph representation matrix is automatically learned to effectively capture the complicated spatial correlations between individual buses within the grid. Then, to help better capture regional SVS features for subsequent learning processes, the adaptive graph representation matrix is properly adjusted by introducing a spatial attention mechanism. Further, with post-fault system trajectory data linked together via attention-based graph representation, a residual spatiotemporal graph convolutional network is carefully built with Optuna-based optimization to deeply mine system-wide spatiotemporal features and thus achieve structure-adaptive SVS assessment. Numerical test results on two representative sub-systems of a realistic provincial power grid in South China demonstrate the efficacy of the proposed approach under various changing topological conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_23204 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adaptive Spatial-Temporal Graph Learning-Enabled Short-Term Voltage Stability Assessment against Time-Varying Topological Conditions Deng, Chao Zhu, Lipeng Liu, Chang Zhai, Hefeng Tian, Baoye Zhu, Zexiang Li, Jiayong Zhang, Cong Systems and Control The emerging deep learning (DL) technology has recently exhibited great potential in data-driven short-term voltage stability (SVS) assessment of complex power grids. However, without sufficient attention to the time-varying topological structures of today's power grids, the majority of existing DL-based SVS assessment schemes could experience severe performance degradation in practice. To address this drawback, this paper proposes an adaptive spatial-temporal graph learning-enabled SVS assessment approach that can adapt well to various topological changes. First, considering the time-varying topological conditions of a given power grid, an adaptive graph representation matrix is automatically learned to effectively capture the complicated spatial correlations between individual buses within the grid. Then, to help better capture regional SVS features for subsequent learning processes, the adaptive graph representation matrix is properly adjusted by introducing a spatial attention mechanism. Further, with post-fault system trajectory data linked together via attention-based graph representation, a residual spatiotemporal graph convolutional network is carefully built with Optuna-based optimization to deeply mine system-wide spatiotemporal features and thus achieve structure-adaptive SVS assessment. Numerical test results on two representative sub-systems of a realistic provincial power grid in South China demonstrate the efficacy of the proposed approach under various changing topological conditions. |
| title | Adaptive Spatial-Temporal Graph Learning-Enabled Short-Term Voltage Stability Assessment against Time-Varying Topological Conditions |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2604.23204 |