Bayesian state estimation for partially observable distribution networks via power flow-informed neural networks
The performance of existing distribution network state estimation (SE) methods is unsatisfactory due to limited real-time measurements. In this paper, a Bayesian SE method is proposed for partially observable distribution networks using a novel power flow-informed neural network (PFINN). The Bayesia...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | International Journal of Electrical Power & Energy Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S014206152500434X |
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| _version_ | 1849390111052005376 |
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| author | Dong Liang Guirong Li Xiaoyu Liu Lin Zeng Hsiao-Dong Chiang Shouxiang Wang |
| author_facet | Dong Liang Guirong Li Xiaoyu Liu Lin Zeng Hsiao-Dong Chiang Shouxiang Wang |
| author_sort | Dong Liang |
| collection | DOAJ |
| description | The performance of existing distribution network state estimation (SE) methods is unsatisfactory due to limited real-time measurements. In this paper, a Bayesian SE method is proposed for partially observable distribution networks using a novel power flow-informed neural network (PFINN). The Bayesian SE model is first established with the goal of directly minimizing the SE error and obtaining the expectation of states conditional on measurements. The conditional expectation is then viewed as a nonparametric regression that can be parameterized by a PFINN where a physics loss penalty is introduced into the loss function to constrain the neural network outputs to be consistent with power network operating constraints. Test results on balanced and unbalanced distribution networks using field data show that the proposed method can achieve better estimation accuracy than the Bayesian SE method without power flow informing. For a fixed neural network, it is always possible to enhance its performance by adding a physics loss term to the original loss function, along with a suitable weight which can be obtained using existing automatic hyperparameter tunning methods. |
| format | Article |
| id | doaj-art-b072bd068db7452289ba062fd7b591fc |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-b072bd068db7452289ba062fd7b591fc2025-08-20T03:41:46ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-09-0117011088610.1016/j.ijepes.2025.110886Bayesian state estimation for partially observable distribution networks via power flow-informed neural networksDong Liang0Guirong Li1Xiaoyu Liu2Lin Zeng3Hsiao-Dong Chiang4Shouxiang Wang5Innovation Research Institute of Hebei University of Technology, Shijiazhuang 050222, China; State Key Laboratory of Intelligent Power Distribution Equipment and System, Hebei Key Laboratory of Equipment and Technology Demonstration of Flexible DC Transmission, Department of Electrical Engineering, Hebei University of Technology, Tianjin 300401, ChinaInnovation Research Institute of Hebei University of Technology, Shijiazhuang 050222, China; State Key Laboratory of Intelligent Power Distribution Equipment and System, Hebei Key Laboratory of Equipment and Technology Demonstration of Flexible DC Transmission, Department of Electrical Engineering, Hebei University of Technology, Tianjin 300401, ChinaState Grid Tangshan Power Supply Company, Tangshan 063000, ChinaSchool of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA; Corresponding author.School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USAKey Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, ChinaThe performance of existing distribution network state estimation (SE) methods is unsatisfactory due to limited real-time measurements. In this paper, a Bayesian SE method is proposed for partially observable distribution networks using a novel power flow-informed neural network (PFINN). The Bayesian SE model is first established with the goal of directly minimizing the SE error and obtaining the expectation of states conditional on measurements. The conditional expectation is then viewed as a nonparametric regression that can be parameterized by a PFINN where a physics loss penalty is introduced into the loss function to constrain the neural network outputs to be consistent with power network operating constraints. Test results on balanced and unbalanced distribution networks using field data show that the proposed method can achieve better estimation accuracy than the Bayesian SE method without power flow informing. For a fixed neural network, it is always possible to enhance its performance by adding a physics loss term to the original loss function, along with a suitable weight which can be obtained using existing automatic hyperparameter tunning methods.http://www.sciencedirect.com/science/article/pii/S014206152500434XPower flow-informed neural networkBayesian state estimationDistribution network |
| spellingShingle | Dong Liang Guirong Li Xiaoyu Liu Lin Zeng Hsiao-Dong Chiang Shouxiang Wang Bayesian state estimation for partially observable distribution networks via power flow-informed neural networks International Journal of Electrical Power & Energy Systems Power flow-informed neural network Bayesian state estimation Distribution network |
| title | Bayesian state estimation for partially observable distribution networks via power flow-informed neural networks |
| title_full | Bayesian state estimation for partially observable distribution networks via power flow-informed neural networks |
| title_fullStr | Bayesian state estimation for partially observable distribution networks via power flow-informed neural networks |
| title_full_unstemmed | Bayesian state estimation for partially observable distribution networks via power flow-informed neural networks |
| title_short | Bayesian state estimation for partially observable distribution networks via power flow-informed neural networks |
| title_sort | bayesian state estimation for partially observable distribution networks via power flow informed neural networks |
| topic | Power flow-informed neural network Bayesian state estimation Distribution network |
| url | http://www.sciencedirect.com/science/article/pii/S014206152500434X |
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