State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks
| Autor: | Azimian, Behrouz, Biswas, Reetam Sen, Moshtagh, Shiva, Pal, Anamitra, Tong, Lang, Dasarathy, Gautam |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | IEEE Transactions on Instrumentation and Measurement, 2022 |
| Druh dokumentu: | Working Paper |
| Popis: | Time-synchronized state estimation for reconfigurable distribution networks is challenging because of limited real-time observability. This paper addresses this challenge by formulating a deep learning (DL)-based approach for topology identification (TI) and unbalanced three-phase distribution system state estimation (DSSE). Two deep neural networks (DNNs) are trained for time-synchronized DNN-based TI and DSSE, respectively, for systems that are incompletely observed by synchrophasor measurement devices (SMDs) in real-time. A data-driven approach for judicious SMD placement to facilitate reliable TI and DSSE is also provided. Robustness of the proposed methodology is demonstrated by considering non-Gaussian noise in the SMD measurements. A comparison of the DNN-based DSSE with more conventional approaches indicates that the DL-based approach gives better accuracy with smaller number of SMDs. Comment: 13 pages. arXiv admin note: substantial text overlap with arXiv:2011.04272 |
| Databáze: | arXiv |
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