State and Topology Estimation for Unobservable Distribution Systems Using Deep Neural Networks
- DOI
- 10.1109/tim.2022.3167722
- Published
- 2022
- Container
- IEEE Transactions on Instrumentation and Measurement
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tim.2022.3167722,
title = {State and Topology Estimation for Unobservable Distribution Systems Using Deep Neural Networks},
author = {Behrouz Azimian and Reetam Sen Biswas and Shiva Moshtagh and Anamitra Pal and Lang Tong and Gautam Dasarathy},
year = {2022},
journal = {IEEE Transactions on Instrumentation and Measurement},
doi = {10.1109/tim.2022.3167722},
url = {https://doi.org/10.1109/tim.2022.3167722}
}RIS
TY - JOUR TI - State and Topology Estimation for Unobservable Distribution Systems Using Deep Neural Networks AU - Behrouz Azimian AU - Reetam Sen Biswas AU - Shiva Moshtagh AU - Anamitra Pal AU - Lang Tong AU - Gautam Dasarathy PY - 2022 JO - IEEE Transactions on Instrumentation and Measurement DO - 10.1109/tim.2022.3167722 UR - https://doi.org/10.1109/tim.2022.3167722 ER -
APA
Azimian, B., Biswas, R. S., Moshtagh, S., Pal, A., Tong, L., & Dasarathy, G. (2022). State and Topology Estimation for Unobservable Distribution Systems Using Deep Neural Networks. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/tim.2022.3167722
Source records
- crossref · retrieved 2026-09-25T17:15:57.397Z