Abstract
Time-synchronized state estimation for reconfigurable distribution networks is challenging because of limited real-time observability. This article 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.
Original language | English (US) |
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Article number | 9003514 |
Journal | IEEE Transactions on Instrumentation and Measurement |
Volume | 71 |
DOIs | |
State | Published - 2022 |
Keywords
- Deep neural network (DNN)
- state estimation
- synchrophasor measurements
- topology identification (TI)
ASJC Scopus subject areas
- Instrumentation
- Electrical and Electronic Engineering