A Novel Data-Driven Approach for Managing Renewable Energy Systems During Short-Term Voltage Instability
This study investigates the application of the maximum Lyapunov exponent in combination with k-means clustering to identify short-term voltage instability in multi-machine power grids. The authors explored the complex regulatory challenges that arise with the integration of renewable energy sources,...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10741538/ |
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| Summary: | This study investigates the application of the maximum Lyapunov exponent in combination with k-means clustering to identify short-term voltage instability in multi-machine power grids. The authors explored the complex regulatory challenges that arise with the integration of renewable energy sources, particularly during short-term voltage instability episodes. The critical role of the maximum Lyapunov exponent in mitigating these risks and bolstering grid resilience was emphasized. This research involved detailed modeling of transient processes within the grid, followed by an in-depth analysis of the resulting data. By integrating maximum Lyapunov exponent with clustering method, this study introduces an automated approach to labeling voltage measurement sets, effectively differentiating between transient regimes, such as short-term voltage instability, normal operational modes, and faults. Extensive numerical experiments conducted using PSCAD simulation software demonstrate the effectiveness of the proposed approach in providing a comprehensive monitoring solution for transient regimes and significantly enhancing stability of the power system with renewable energy sources. |
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| ISSN: | 2169-3536 |