Physics-Informed Neural Networks for Accelerating Power System State Estimation
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866908935109214208 |
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| author | Falas, Solon Asprou, Markos Konstantinou, Charalambos Michael, Maria K. |
| author_facet | Falas, Solon Asprou, Markos Konstantinou, Charalambos Michael, Maria K. |
| contents | State estimation is the cornerstone of the power system control center since it provides the operating condition of the system in consecutive time intervals. This work investigates the application of physics-informed neural networks (PINNs) for accelerating power systems state estimation in monitoring the operation of power systems. Traditional state estimation techniques often rely on iterative algorithms that can be computationally intensive, particularly for large-scale power systems. In this paper, a novel approach that leverages the inherent physical knowledge of power systems through the integration of PINNs is proposed. By incorporating physical laws as prior knowledge, the proposed method significantly reduces the computational complexity associated with state estimation while maintaining high accuracy. The proposed method achieves up to 11% increase in accuracy, 75% reduction in standard deviation of results, and 30% faster convergence, as demonstrated by comprehensive experiments on the IEEE 14-bus system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_03088 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Physics-Informed Neural Networks for Accelerating Power System State Estimation Falas, Solon Asprou, Markos Konstantinou, Charalambos Michael, Maria K. Machine Learning Systems and Control State estimation is the cornerstone of the power system control center since it provides the operating condition of the system in consecutive time intervals. This work investigates the application of physics-informed neural networks (PINNs) for accelerating power systems state estimation in monitoring the operation of power systems. Traditional state estimation techniques often rely on iterative algorithms that can be computationally intensive, particularly for large-scale power systems. In this paper, a novel approach that leverages the inherent physical knowledge of power systems through the integration of PINNs is proposed. By incorporating physical laws as prior knowledge, the proposed method significantly reduces the computational complexity associated with state estimation while maintaining high accuracy. The proposed method achieves up to 11% increase in accuracy, 75% reduction in standard deviation of results, and 30% faster convergence, as demonstrated by comprehensive experiments on the IEEE 14-bus system. |
| title | Physics-Informed Neural Networks for Accelerating Power System State Estimation |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2310.03088 |