A Resilient Deep Learning Approach for State Estimation in Distribution Grids With Distributed Generation
State estimation is a challenging problem, particularly in distribution grids that have unique characteristics compared with transmission grids. Conventional methods that solve the state estimation problem at the transmission level require the grid to be observable, which does not apply to distribut...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/etep/2734170 |
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| _version_ | 1850189128655699968 |
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| author | Ronald Kfouri Harag Margossian |
| author_facet | Ronald Kfouri Harag Margossian |
| author_sort | Ronald Kfouri |
| collection | DOAJ |
| description | State estimation is a challenging problem, particularly in distribution grids that have unique characteristics compared with transmission grids. Conventional methods that solve the state estimation problem at the transmission level require the grid to be observable, which does not apply to distribution grids. To make the distribution grid observable, researchers resort to pseudomeasurements, which are inaccurate. Also, the high integration of renewable energy introduces uncertainty, making the Distribution System State Estimation (DSSE) problem even more complex. This work proposes a deep neural network approach that solves the DSSE problem in unobservable distribution grids without employing erroneous pseudomeasurements. We create a dataset that emulates real-life scenarios of diverse operating conditions with distributed generation. We then subject the neural network to multiple test scenarios featuring noisier measurements and bad data to evaluate the robustness of our algorithm. We test our approach on three networks. Results demonstrate that our method efficiently solves the DSSE problem—which cannot be solved using conventional methods—and detects and mitigates bad data, further enhancing the reliability of the state estimation results. |
| format | Article |
| id | doaj-art-25b3bbf5ff144fca8431049331d24a05 |
| institution | OA Journals |
| issn | 2050-7038 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-25b3bbf5ff144fca8431049331d24a052025-08-20T02:15:41ZengWileyInternational Transactions on Electrical Energy Systems2050-70382025-01-01202510.1155/etep/2734170A Resilient Deep Learning Approach for State Estimation in Distribution Grids With Distributed GenerationRonald Kfouri0Harag Margossian1Department of Electrical and Computer EngineeringDepartment of Electrical and Computer EngineeringState estimation is a challenging problem, particularly in distribution grids that have unique characteristics compared with transmission grids. Conventional methods that solve the state estimation problem at the transmission level require the grid to be observable, which does not apply to distribution grids. To make the distribution grid observable, researchers resort to pseudomeasurements, which are inaccurate. Also, the high integration of renewable energy introduces uncertainty, making the Distribution System State Estimation (DSSE) problem even more complex. This work proposes a deep neural network approach that solves the DSSE problem in unobservable distribution grids without employing erroneous pseudomeasurements. We create a dataset that emulates real-life scenarios of diverse operating conditions with distributed generation. We then subject the neural network to multiple test scenarios featuring noisier measurements and bad data to evaluate the robustness of our algorithm. We test our approach on three networks. Results demonstrate that our method efficiently solves the DSSE problem—which cannot be solved using conventional methods—and detects and mitigates bad data, further enhancing the reliability of the state estimation results.http://dx.doi.org/10.1155/etep/2734170 |
| spellingShingle | Ronald Kfouri Harag Margossian A Resilient Deep Learning Approach for State Estimation in Distribution Grids With Distributed Generation International Transactions on Electrical Energy Systems |
| title | A Resilient Deep Learning Approach for State Estimation in Distribution Grids With Distributed Generation |
| title_full | A Resilient Deep Learning Approach for State Estimation in Distribution Grids With Distributed Generation |
| title_fullStr | A Resilient Deep Learning Approach for State Estimation in Distribution Grids With Distributed Generation |
| title_full_unstemmed | A Resilient Deep Learning Approach for State Estimation in Distribution Grids With Distributed Generation |
| title_short | A Resilient Deep Learning Approach for State Estimation in Distribution Grids With Distributed Generation |
| title_sort | resilient deep learning approach for state estimation in distribution grids with distributed generation |
| url | http://dx.doi.org/10.1155/etep/2734170 |
| work_keys_str_mv | AT ronaldkfouri aresilientdeeplearningapproachforstateestimationindistributiongridswithdistributedgeneration AT haragmargossian aresilientdeeplearningapproachforstateestimationindistributiongridswithdistributedgeneration AT ronaldkfouri resilientdeeplearningapproachforstateestimationindistributiongridswithdistributedgeneration AT haragmargossian resilientdeeplearningapproachforstateestimationindistributiongridswithdistributedgeneration |